External Linking Based On Hierarchical Level Weightings

ABSTRACT

Certain implementations of the disclosed technology include systems and methods for external linking based on hierarchal level weightings. The method may include associating external query data having one or more query field values with a record in a linked hierarchical database. The linked hierarchical database may include a plurality of records, each record having a record identifier and representing an entity in a hierarchy, each record associated with a hierarchy level, each record including one or more fields, each field configured to contain a field value. The associating may include receiving the external query data, wherein the external query data includes one or more search values; and identifying, from the plurality of records in the linked hierarchical database, one or more matched fields having field values that at least partially match the one or more search values.

RELATED APPLICATIONS

This application is related to U.S. Pat. No. 8,316,047 filed on Apr. 24,2009, entitled: “Adaptive Clustering of Records and EntityRepresentations.” This application is also related to U.S. PatentPublication 20100094910, filed on Dec. 14, 2009. The contents of thesedocuments are hereby incorporated by reference in their entirety as ifset forth in full.

BACKGROUND

One of the most difficult and complex tasks in a data processingenvironment involves the data integration process of accuratelymatching, linking, and/or clustering records from multiple data sourcesthat refer to a person, a business, a hierarchical structure or otherentity.

Certain forms of data may be used to represent a hierarchy. A hierarchyis a general term that can be used to describe an arrangement ofentities at various levels within a given structure. A hierarchy may beutilized to describe many types of phenomena, organizations, structures,processes, etc. For example, a business may be represented by anorganization chart in which the various levels of the business may bedefined by functions, seniority, locations, direct reports, etc.

External linking, which is sometimes referred to as “entity resolution,”may be utilized for resolving entities within a hierarchical structure.External linking may involve a process of linking information from anexternal file to a previously linked base file (or authority file) inorder to assign entity identifiers to the external data. In certainembodiments, an external linking process may act upon a file created byan internal linking process. For example, and according to certainexample implementations, an internal linking process may be utilized asinitial process to characterize or group data when data relationshipsare not known beforehand. In an example implementation, an externallinking process may be utilized after at least some data relationshipsare established by the internal linking process.

SUMMARY

Some or all of the above needs may be addressed by certainimplementations of the disclosed technology. Certain implementations mayinclude systems and methods for external linking based on hierarchallevel weightings. According to an example implementation, a method isprovided. The method may include associating external query data havingone or more query field values with a record in a linked hierarchicaldatabase. The linked hierarchical database may include a plurality ofrecords, each record having a record identifier and representing anentity in a hierarchy, each record associated with a hierarchy level,each record including one or more fields, each field configured tocontain a field value. The associating may include receiving theexternal query data, wherein the external query data includes one ormore search values; and identifying, from the plurality of records inthe linked hierarchical database, one or more matched fields havingfield values that at least partially match the one or more searchvalues.

The method may further include scoring, with zero or more match weights,each of the one or more matched fields; determining an aggregate weightfor each matched field based at least in part on the scoring with thezero or more match weights; sorting the one or more matched fieldsaccording to the determined aggregate weights; merging, based at leastin part on determining the aggregate weights, the one or more matchedfields to form a merged table having records with matched fields sortedby aggregate weights; scoring the merged table based at least in part onthe aggregate weights; identifying, based at least in part on thescoring, a grouping comprising one or more of the plurality of entitieswithin a same branch of the hierarchy and corresponding to differenthierarchy levels; and outputting, based at least in part on the scoringand identifying, a record identifier corresponding to a matching entityin the hierarchy.

According to another example implementation, a system is provided. Thesystem includes at least one memory for storing data andcomputer-executable instructions; and at least one processor configuredto access the at least one memory and further configured to execute thecomputer-executable instructions to cause the system to perform a methodthat includes associating external query data having one or more queryfield values with a record in a linked hierarchical database, the linkedhierarchical database comprising a plurality of records, each recordhaving a record identifier and representing an entity in a hierarchy,each record associated with a hierarchy level, each record comprisingone or more fields, each field configured to contain a field value. Theassociating may further include receiving the external query data,wherein the external query data comprises one or more search values; andidentifying, from the plurality of records in the linked hierarchicaldatabase, one or more matched fields having field values that at leastpartially match the one or more search values.

The method performed by the system may further include scoring, withzero or more match weights, each of the one or more matched fields;determining an aggregate weight for each matched field based at least inpart on the scoring with the zero or more match weights; sorting the oneor more matched fields according to the determined aggregate weights;merging, based at least in part on determining the aggregate weights,the one or more matched fields to form a merged table having recordswith matched fields sorted by aggregate weights; scoring the mergedtable based at least in part on the aggregate weights; identifying,based at least in part on the scoring, a grouping comprising one or moreof the plurality of entities within a same branch of the hierarchy andcorresponding to different hierarchy levels; and outputting, based atleast in part on the scoring and identifying, a record identifiercorresponding to a matching entity in the hierarchy.

According to another example implementation a non-transitorycomputer-readable media is provided. The computer-readable media iscapable of storing computer-executable instructions that, when executedby one or more processors, cause the one or more processors to perform amethod that may include associating external query data having one ormore query field values with a record in a linked hierarchical database,the linked hierarchical database comprising a plurality of records, eachrecord having a record identifier and representing an entity in ahierarchy, each record associated with a hierarchy level, each recordcomprising one or more fields, each field configured to contain a fieldvalue. The associating may include receiving the external query data,wherein the external query data comprises one or more search values; andidentifying, from the plurality of records in the linked hierarchicaldatabase, one or more matched fields having field values that at leastpartially match the one or more search values. scoring, with zero ormore match weights, each of the one or more matched fields;

The computer-executable instructions, when executed by one or moreprocessors, further cause the one or more processors to perform a methodincluding determining an aggregate weight for each matched field basedat least in part on the scoring with the zero or more match weights;sorting the one or more matched fields according to the determinedaggregate weights; merging, based at least in part on determining theaggregate weights, the one or more matched fields to form a merged tablehaving records with matched fields sorted by aggregate weights; scoringthe merged table based at least in part on the aggregate weights;identifying, based at least in part on the scoring, a groupingcomprising one or more of the plurality of entities within a same branchof the hierarchy and corresponding to different hierarchy levels; andoutputting, based at least in part on the scoring and identifying, arecord identifier corresponding to a matching entity in the hierarchy.

Other implementations, features, and aspects of the disclosed technologyare described in detail herein and are considered a part of the claimeddisclosed technology. Other implementations, features, and aspects canbe understood with reference to the following detailed description,accompanying drawings, and claims.

BRIEF DESCRIPTION OF THE FIGURES

Reference will now be made to the accompanying figures and flowdiagrams, which are not necessarily drawn to scale, and wherein:

FIG. 1A is a block diagram of an illustrative clustering process,according to an example implementation of the disclosed technology.

FIG. 1B is a block diagram of a clustering process, according to anexample implementation of the disclosed technology.

FIG. 2A is a block diagram of an illustrative tree-type organizationchart according to an example embodiment of the disclosed technology.

FIG. 2B is a block diagram depicting an illustrative entities in anorganization, where the hierarchy structure is not available and thedata is incomplete.

FIG. 2C is a block diagram depicting illustrative entities (as includedin FIG. 2A), where the hierarchy structure is partially known ordetermined, according to an example implementation of the disclosedtechnology.

FIG. 2D is another block diagram depicting illustrative entities (asincluded in FIG. 2A), where the hierarchy structure is partially knownor determined, according to an example implementation of the disclosedtechnology.

FIG. 2E is a block diagram depicting an example complex hierarchystructure.

FIG. 3 is a block diagram depicting an example organization andassociated hierarchical linkages.

FIG. 4A depicts an example implementation of an external linkingprocess, according to an example embodiment of the disclosed technology.

FIG. 4B depicts an external linking process, in accordance with anexample implementation of the disclosed technology, in which theentities may form a hierarchy.

FIG. 5A depicts example hierarchical structures for illustrationpurposes.

FIG. 5B depicts an intermediate result of an external linking processbased on hierarchal level weightings, according to an exampleimplementation of the disclosed technology.

FIG. 5C depicts example results of an external linking process based onhierarchal level weightings, according to an example implementation ofthe disclosed technology.

FIG. 6 is a flow diagram of a method according to an exampleimplementation of the disclosed technology.

FIG. 7 is a flow diagram of another method according to an exampleimplementation of the disclosed technology.

FIG. 8 is a flow diagram of another method according to an exampleimplementation of the disclosed technology.

FIG. 9 is a flow diagram of another method according to an exampleimplementation of the disclosed technology.

FIG. 10 is a flow diagram of another method according to an exampleimplementation of the disclosed technology.

FIG. 11 is a block diagram of an illustrative computing system,according to an example implementation of the disclosed technology.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully hereinafter with reference to the accompanying drawings. Thisdisclosed technology may, however, be embodied in many different formsand should not be construed as limited to the implementations set forthherein.

In the following description, numerous specific details are set forth.However, it is to be understood that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one implementation,” “an implementation,”“example implementation,” “various implementations,” etc., indicate thatthe implementation(s) of the disclosed technology so described mayinclude a particular feature, structure, or characteristic, but notevery implementation necessarily includes the particular feature,structure, or characteristic. Further, repeated use of the phrase “inone implementation” does not necessarily refer to the sameimplementation, although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected,” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled,” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form.

The various embodiments disclosed herein may apply to a wide variety ofapplications, including but not limited to data analytics, entityresolution, entity searching, removal of duplicate records, andincreasing the speed and accuracy of such applications.

1A. Internal Co-Convergence Using Clustering (with No Hierarchy in theData Structure)

According to certain example implementations of the disclosedtechnology, record linking and clustering may involve an internallinking process. In an example implementation of the disclosedtechnology, internal linking may include (1) determining relationshipsamong available data; (2) computing an aggregate relationship betweenany two entities; and (3) partitioning the data (and/or the spacedefined by the data) based upon the relationships.

For example, an internal linking processes may receive or utilize allavailable information and partition or cluster the data according todifferent entities and/or relationships among the different entities. Inone example implementation, the data may be evaluated for itsrelationship to a particular entity, and the data may be grouped into acluster based on certain characteristics or similarities with members ofthe cluster. In accordance with an example implementation of thedisclosed technology, one of the core features of co-convergence is thatthe cluster sets may represent different entities. In one exampleimplementation, co-convergence may involve two different processesclustering two different entity spaces that are collaborating.

In certain embodiments, a general goal of co-convergence may be tocluster related records, then re-cluster the clusters without ruiningthe original cluster. Co-convergence may be utilized to understand partsof a structure, but it may be difficult to understand how the parts fittogether without knowledge of the structure. In an exampleimplementation, co-convergence may elicit the structure from therelationships of the parts.

Certain examples of co-convergence using clustering may be applied to asingle set of data records. Other examples of co-convergence usingclustering may be applied to two or more sets of data records. Forexample, a single record set might include fields such as Name, Addressand may be co-converged to elicit people and places. In another exampleimplementation, two completely different record sets may be co-convergedat the same time. For example, one set of data may represent people andinclude fields such as First Name, Last Name, Address, and another setof data may represent businesses and include fields such as BusinessName, Address. Certain example implementations of the disclosedtechnology may allow converging separate entity spaces in sync, where incertain embodiments, the separate entity spaces may be in differentrecord sets.

FIG. 1A and FIG. 1B provide example graphical representations of aclustering and co-convergence process, according to an exampleimplementation of the disclosed technology. FIG. 1A depicts an exampleinitial clustering (with no real co-convergence implied). The circlesshown in FIG. 1A may depict available database record representations102 corresponding to two or more different entities. As indicated above,such records may be in a single record set, or they may be from two ormore record sets. Such database record representations 102 may beexamined and compared to determine linkages or relationships among therecords. The “relationships” among the various records (nodes) may berepresented (for illustration purposes) as connecting lines (edges),with line weights representing different types of relationships and/orweightings among field values of the database records.

In certain example embodiments, each of the record data representations102 may include multiple fields (not shown in FIG. 1A), and maytherefore be represented as nodes in a hyperspace. In one exampleimplementation, the record data representations 102 may relate toentities, such as people, and may include fields (such as Last Name,First Name, Address, Social Security Number, etc.,) with correspondingfield values (such as Smith, John, 45 Broad Street, 543-21-1111). Inanother example implementation, the record data representations 102 mayrepresent entities such as an organization, and may include fields suchas corporate offices, branches, locations, products, etc., withcorresponding field values. In other example embodiments, the recorddata representations 102 may include data representations from two ormore different record sets. For example, and as previously discussed,the data may include representations from one set of records thatrepresent people (with fields such as Last Name, First Name, Address,Social Security Number, etc.,) and the data may include representationsfrom another set of records that represent businesses (with fields suchas corporate offices, branches, locations, products, etc.).

According to certain example implementations, each available record datarepresentation 102 may correspond to an entity representation and mayinclude a plurality of fields, each field configured to contain a fieldvalue, and each field value assigned a field value weight correspondingto a specificity of the field value in relation to all field values in acorresponding field of the records.

In accordance with an example implementation, for any particular givenrecord attribute, the general process of clustering records may berefined with each iteration by assuming that all the other records andrelationships are correct, performing one clustering iteration, thenmoving on to the next record attribute, performing one clusteringiteration, and so forth. For example, referring again to FIG. 1A, therecord data representations 102 may be evaluated with respect to aparticular attribute and a cluster of records may be identified ashaving certain quantitative or qualitative relationships to theparticular attribute of interest.

An example of an initial cluster 106 is depicted in FIG. 1. The sameinitial cluster 106 is shown contained within a dotted outline 110 todistinguish the members of the cluster from the remaining records 108.The initial clustered records 106, as depicted in this example, areshown sharing a common attribute identifier: “A,” along with connectionweights that may represent any number of scenarios, according to certainexample embodiments of the disclosed technology. For example, the “A”identifier and the connecting edges may represent certain commonalitieswith respect to the identifier evaluated in the clustering iteration(such as exact or partial matches of a last name).

FIG. 1B depicts another graphical example of an additional firstiteration clustering 112 of record data representations 102 in the dataset(s) having an attribute identifier depicted as “C.” FIG. 1B alsodepicts a re-clustering iteration process, according to an exampleimplementation of the disclosed technology, in which a new cluster 114is formed having records identified with both “A” and “C” attributes. Toarrive at the new cluster 114 (and not explicitly shown in FIG. 1B),example embodiments may utilize a first iteration process wherebyrecords with “A” attributes are clustered while noting relationships(edges and weights, for example) between those records having “C”attributes, and vice-versa. For example, starting with the initialcluster 110, attributes or commonalities (represented by connectingedges) may be evaluated to aggregate one or more relationships betweenany two entities. As depicted in FIG. 1B, and based on relationshipsand/or other criteria among the records, the new cluster 114 formed inthe re-clustering step may include certain records of the firstiteration clusters 110 112 while omitting certain records 116.

In general terms, and in accordance with an example implementation, theavailable records 102 (as shown in FIG. 1A) may be initially clusteredinto a first set of clusters having corresponding first clusteridentifications (IDs), and each record may include one or more fieldvalues. For example, records may be clustered according to the variousidentifications, such as “A,” “B,” “C,” “D,” etc., as indicated in FIG.1A. In accordance with an example implementation, and as discussed abovewith respect to FIGS. 1A and 1B, the initial clustering iteration(s) maybe based at least in part on determining similarity among correspondingfield values of database records. In an example implementation, mutuallymatching records may be associated by performing at least one matchingiteration for each of the records 102, based at least in part on thecluster IDs. In an example implementation, the matching iteration mayinclude linking related database records based at least in part on adetermined match value. In another example implementation, the matchingiteration may include linking related database records, based at leastin part on determined mutually preferred records. In an exampleimplementation, the clustering may include a process of determiningsimilarity among corresponding field values of the database records.

According to an example implementation of the disclosed technology, theiteration process may include re-clustering at least a portion of thedatabase records into a second set of clusters (for example, the cluster114 shown in FIG. 1B) having a corresponding second cluster ID. In anexample implementation, the re-clustering may be based, at least inpart, on associating mutually matching attributes of the initialclusters. In another example implementation, the re-clustering may bebased, at least in part, on determining similarity among correspondingfield values of the database records.

In one example implementation, the initial clustering may includeassociating mutually matching database records, which may includedetermining highest compelling linkages among the database records,which may further include identifying mutually preferred pairs ofrecords from the database records, each mutually preferred pair ofrecords consisting of a first record and a second record, the firstrecord consisting of a preferred record associated with the secondrecord and the second record consisting of a preferred record associatedwith the first record. In an example implementation, the mutuallypreferred pairs of records may be assigned a match score that meetspre-specified match criteria.

In an example implementation, the iteration process may also includeassigning, for each record from the database records, at least oneassociated preferred record, wherein a match value assigned to a givenrecord together with its associated preferred record is at least asgreat as a match value assigned to the record together with any otherrecord in the database records. In an example implementation, theiteration process may also include forming and storing a plurality ofentity representations in the database, each entity representation ofthe plurality of entity representations including at least one linkedpair of mutually preferred records.

According to an example implementation of the disclosed technology,determining similarity among the corresponding field values of therecords 102 may include assigning a hyperspace attribute to each record102. The hyperspace attribute that corresponds to two database recordsmay correlate with a similarity of the corresponding field values of thetwo database records. In certain example embodiments, membership of eachdatabase record in a plurality of hyperspace clusters may be determinedbased at least in part on the hyperspace attributes. According to anexample implementation each record 102 may be assigned a cluster ID anda match value reflecting a likelihood that the record is a member of aparticular hyperspace cluster, and related records may be linked basedat least in part on the cluster ID and match value (as depicted by theedges joining the nodes in FIG. 1A). Determining membership of eachdatabase record in the plurality of hyperspace clusters, for example,may include creating a plurality of nodes at random locations inhyperspace, each node maintaining records in hyperspace based on thehyperspace attribute for which it is the closest node.

In accordance with certain implementations of the disclosed technologyduplicate records (for example, ones that are likely to represent thesame entity) may be eliminated by merging those database records thathave hyperspace attribute differences within a predefined criteria,resulting in a reduced set of database records. In accordance with anexample implementation, the process may further include recalculatingthe field value weights for the reduced set of database records, andre-clustering the reduced set of records based at least in part on therecalculated field value weights.

According to an example implementation, of the disclosed technology, theclustering, iterating, recalculating, and re-clustering etc. may producea set of refined clusters in which the records in a given set possesscriteria that resemble the other records in the set. Such clustering mayprovide useful characteristics, categories, structures, etc., forunderstanding the interrelations among records in a database, and mayfurther be used to define characteristics, categories, structures, etc.,for new data as it becomes available. Additional support anddescriptions of the disclosed technology may be found in U.S. Pat. No.8,316,047, incorporated herein by reference.

An example method 600, that may be utilized for providing internalco-convergence using clustering with no hierarchy in the data structure,will now be described with reference to the flowchart of FIG. 6. Themethod 600 starts in block 602, and according to an exampleimplementation includes clustering database records into a first set ofclusters having corresponding first cluster identifications (IDs), eachdatabase record including one or more field values, wherein theclustering is based at least in part on determining similarity amongcorresponding field values of the database records. In block 604, themethod 600 includes associating mutually matching database records,wherein the associating includes performing at least one matchingiteration for each of the database records, wherein the matchingiteration is based at least in part on the first cluster IDs. In block606, the method 600 includes determining similarity among correspondingfield values of the database records. In block 608, the method 600includes re-clustering at least a portion of the database records into asecond set of clusters having corresponding second cluster IDs, there-clustering based at least in part on the associating mutuallymatching database records and on the determining similarity amongcorresponding field values of the database records. In block 610, themethod 600 includes outputting database record information, based atleast in part on the re-clustering.

In certain example implementations of the disclosed technology,determining similarity among the corresponding field values of thedatabase records may include assigning a hyperspace attribute to eachdatabase record, wherein the hyperspace attribute corresponding to twodatabase records is correlated with a similarity of the correspondingfield values of the two database records; determining membership of eachdatabase record in a plurality of hyperspace clusters based at least inpart on the hyperspace attributes; assigning, to each record, a clusterID and a match value reflecting a likelihood that the record is a memberof a particular hyperspace cluster; and linking related records based atleast in part on the cluster ID and match value.

Certain example implementations may include merging database recordshaving hyperspace attribute differences within a predefined criteria toeliminate similar exemplars that are likely to represent a same entity,the merging resulting in a reduced set of database records. An exampleembodiment may include recalculating the field value weights for thereduced set of database records and re-clustering the reduced set ofrecords based at least in part on the recalculated field value weights.

In certain example implementations of the disclosed technology,determining membership of each database record in the plurality ofhyperspace clusters may further include creating a plurality of nodes atrandom locations in hyperspace, each node maintaining records inhyperspace based on the hyperspace attribute for which it is the closestnode.

In accordance with an example implementation, associating mutuallymatching database records may further include determining highestcompelling linkages among the database records. In certain exampleembodiments, the determining may include identifying mutually preferredpairs of records from the database records, each mutually preferred pairof records consisting of a first record and a second record, the firstrecord consisting of a preferred record associated with the secondrecord and the second record consisting of a preferred record associatedwith the first record, wherein the mutually preferred pairs of recordseach has a match score that meets pre-specified match criteria. Incertain example embodiments, the determining may include assigning, foreach record from the database records, at least one associated preferredrecord, wherein a match value assigned to a given record together withits associated preferred record is at least as great as a match valueassigned to the record together with any other record in the databaserecords. In certain example embodiments, the determining may includeforming and storing a plurality of entity representations in thedatabase, each entity representation of the plurality of entityrepresentations comprising at least one linked pair of mutuallypreferred records.

According to an example implementation, each database record maycorresponds to an entity representation, each database record comprisinga plurality of fields, each field configured to contain a field value,and each field value assigned a field value weight corresponding to aspecificity of the field value in relation to all field values in acorresponding field of the records.

In an example implementation, performing the at least one matchingiteration may include linking related database records based at least inpart on a determined match value or determined mutually preferredrecords.

1B. Internal Co-Convergence Using Clustering (with Hierarchy in the DataStructure)

FIG. 2A depicts a hypothetical tree-type organization chart 200 that maybe utilized to illustrate a process of internal co-convergence whenthere is a hierarchy in the data structure, according to an exampleembodiment of the disclosed technology. The various related entities ofthe hypothetical organization are also tabulated in Table 1 with theassociated reference numerals indicated in the left hand column.

TABLE 1 (See FIG. 2A) Ref. # ID Direct Report City Product 202 Corp.Office New York ABC, CDE, XYZ 204 Company 1 Corp. Office Atlanta ABCSoftware 220 Company 2 Corp. Office New York CDE Electronics 250 Company3 Corp. Office Chicago XYZ Paper 208 Branch 1A Company 1 Salt Lake ABCSoftware 206 Branch 1B Company 1 Dallas ABC Software 232 Branch 2ACompany 2 San Diego CDE Electronics 222 Branch 2B Company 2 New York CDEElectronics 252 Branch 3A Company 3 New York XYZ Paper 210 Manager 1ABranch 1A Denver ABC Software 234 Manager 2A Branch 2A Dallas CDEElectronics 224 Manager 2B Branch 2B New York CDE Electronics 254Manager 3A Branch 3A Atlanta XYZ Paper 212 Assistant 1A1 Manager 1ADallas ABC Software 238 Assistant 2A1 Manager 2A Dallas CDE Electronics236 Assistant 2A2 Manager 2A New York CDE Electronics 226 Assistant 2B1Manager 2B Salt Lake CDE Electronics 228 Assistant 2B2 Manager 2B DenverCDE Electronics 256 Employee 3A1 Manager 3A New York XYZ Paper 214Intern 1A1 Assistant 1A1 Salt Lake ABC Software 240 Intern 2A2 Assistant2A1 Salt Lake CDE Electronics 230 Intern 2B2 Assistant 2B2 New York CDEElectronics

As indicated in FIG. 2A, the hypothetical organization includes acorporate office 202 having three branches: “Company 1” 204, “Company2”, 220, and “Company 3” 250, with respective hypothetical product linesof ABC software, CDE electronics, and XYZ paper. Each company in thehypothetical organization also includes branch offices, as indicated inthe chart 200 of FIG. 2A and in Table 1. The organization chart 200 alsoincludes people, such as managers, assistants, interns, and employees,with respective levels and position in the organization hierarchystructure. The information shown in Table 1 could be utilized toreproduce a chart (similar to that of chart 200) based on the “directreport” relationships in the hierarchy.

The information shown in FIG. 2A (and tabulated in Table 1) mayrepresent a complete and accurate picture of the hypotheticalorganization. However, in certain cases, limited or incomplete data maybe available, without the benefit of knowing the hierarchical structureor interrelation among the entities. Embodiments of the disclosedtechnology may be utilized to piece-together, approximate, or at leastpartially determine the organizational structure based on incompletedata. Embodiments of the disclosed technology may be utilized to refineapproximated hierarchical relationships, as new information becomesavailable.

FIG. 2B depicts illustrative entities in an organization, where thehierarchy structure is not yet available and the data may be incomplete(or only partially utilized). The entities shown in FIG. 2B are alsotabulated in Table 2, with the reference numerals indicated in the lefthand column. This information represents a typical situation where itmay be desired to determine one or more relationships in theorganizational structure based on limited information. In certainembodiments, the available information (for example, as depicted in FIG.2B) may be insufficient to gain any further insights into the structureof the organization due to the lack of parent-child-sibling linkinginformation in the data. In the case of FIG. 2B (and Table 2), theinability to connect entities is further exacerbated by the differentcity location of the companies, branches, managers, etc., and additionalinformation may be needed to derive connections among the entities.

As an illustrative example, and based on the available informationtabulated in Table 2, a logical assumption may be to link the Manager 3A253 with Company 1 204 based on the common city designation. However,according to the actual hierarchy relationships shown in FIG. 2A,Manager 3A 253 is actually linked with Company 3 250 via Branch 3A 252.As will be illustrated below, initial linkages that are based on limitedinformation may be revised as additional information becomes availableand is utilized in the process of determining the hierarchy structureand relationships among the entities in the hierarchy.

TABLE 2 (See FIG. 2B) Ref. # ID City 204 Company 1 Atlanta 250 Company 3Chicago 206 Branch 1B Dallas 232 Branch 2A San Diego 222 Branch 2B NewYork 210 Manager 1A Denver 254 Manager 3A Atlanta 238 Assistant 2A1Dallas 236 Assistant 2A2 New York 226 Assistant 2B1 Salt Lake 256Employee 3A1 New York 214 Intern 1A1 Salt Lake 240 Intern 2A2 Salt Lake230 Intern 2B2 New York

FIG. 2C depicts illustrative entities in the hypothetical organization(as included in FIG. 2A), where the information is still incomplete, butthe available “direct report” information is utilized to fill-in some ofthe hierarchical structure. The entities shown in FIG. 2C are tabulatedin Table 3, with the reference numerals indicated in the left handcolumn. It should be emphasized that this example is for illustrationpurposes and to further provide a foundation for further explanation ofcertain implementations of the disclosed technology.

Based on the available and/or utilized information about thehypothetical organization, and as depicted in FIG. 2C and Table 3,certain connections among the entities may be directly determined orassumed, and a slightly more detailed view of the organization may berevealed. For example, the data in Table 3 indicates that “Manager 3A”254, in Atlanta, is shown as a direct report to “Branch 3A,” and thus aparent-child connection 280 may be drawn connecting “Manager 3A” 254,and a (yet unknown) parent entity 252. In this case, the actual entityrepresentation identification (ID) for “Branch 3A” is missing from thedata, but since it is included as a direct report for “Manager 3A” 254,it may be assumed that this parent entity 254 may be “*Branch 3A” 252(and the “*” symbol may designate an assumed or derived value based onthe given direct report information. A similar process may be utilizedto fill-in the known or assumed hierarchical connections 282, 284, 286and additional (yet unknown) entities 234, 252 according to an exampleimplementation of the disclosed technology.

TABLE 3 (See FIG. 2C) Ref # ID Direct Report City Product 204 Company 1Atlanta 250 Company 3 Chicago 223 Company X? 233 Company Y? 206 Branch1B Dallas 232 Branch 2A Company Y? San Diego CDE Electronics 222 Branch2B Company X? New York CDE Electronics 210 Manager 1A Denver 252 ? 254Manager 3A Branch 3A Atlanta 238 Assistant 2A1 Dallas 236 Assistant 2A2Manager 2A New York 226 Assistant 2B1 Salt Lake 256 Employee 3A1 Manager3A New York 234 ? 214 Intern 1A1 Salt Lake 240 Intern 2A2 Assistant 2A1Salt Lake 230 Intern 2B2 New York

FIG. 2D depicts illustrative entities in the hypothetical organization(as included in FIG. 2A), where the information is still incomplete, butadditional information may become available and/or may be utilized tofill-in additional hierarchical structure. The entities shown in FIG. 2Dare also tabulated in Table 4, with the reference numerals indicated inthe left hand column.

Based on the newly available (and/or newly utilized) information aboutthe hypothetical organization, and as depicted in FIG. 2D and Table 4,certain additional connections among the entities may be directlydetermined or assumed, and a more detailed view of the organization maybe revealed. In this example, the data shown in Table 4 shows theinformation from Table 3, but now includes a new entry of “Branch 1A”208, in Salt Lake that reports directly to “Company 1” 204. Furthermore,the new data includes “Assistant 1A1” 212 in Dallas, who reports to“Manager 1A” 210 in Denver, who reports to “Branch 1A” 208 in Salt Lake.Based on this additional information, a more complete hierarchystructure below “Company 1” 204 may now be realized.

In this example, the new data also provides parent-child linkinginformation that indicates that “Branch 2B” 222 in New York reports tosome “Company X?” 223, and that “Branch 2A” 232 in San Diego report tosome “Company Y?” 233. The illustrative information shown in Table 3also indicates that the entities “Branch 2B” 222 “Branch 2A” 232 areinvolved with a common product “CDE Electronics.” Based on this (andpossibly other) compelling information, and according to an exampleimplementation of the disclosed technology, it may be inferred that theparent entities “Company X?” 223 and “Company Y?” 233 are in fact, thesame entity. This example of associating related hierarchical databaserecords by applying a hierarchal directional linking process isillustrated in FIG. 2D and Table 4, with a placeholder for this newentity “Company 2?” 220 that is a reduction of the separate entities“Company X?” 223 and “Company Y?” 233 and referred in relation to thelinking relationships 288 from “Branch 2B” 222 in New York and “Branch2A” 232 in San Diego.

TABLE 4 (See FIG. 2D) Ref # ID Direct Report City 202 Headquarters? 204Company 1 Atlanta 220 Company 2? 250 Company 3 Chicago 208 Branch 1ACompany 1 Salt Lake 206 Branch 1B Company 1 Dallas 232 Branch 2A Company2 San Diego 222 Branch 2B Company 2 New York 252 Branch 3A Company 3 NewYork 210 Manager 1A Branch 1A Denver 234 Manager 2A? 224 Manager 2B? 254Manager 3A Branch 3A Atlanta 212 Assistant 1A1 Manager 1A Dallas 238Assistant 2A1 Manager 2A Dallas 236 Assistant 2A2 Manager 2A New York226 Assistant 2B1 Manager 2B Salt Lake 228 Assistant 2B2 Manager 2BDenver 256 Employee 3A1 Manager 3A New York 214 Intern 1A1 Assistant 1A1Salt Lake 240 Intern 2A2 Assistant 2A1 Salt Lake 230 Intern 2B2Assistant 2B2 New York

As indicated in FIG. 2D and in Table 4, new data (or newly utilizeddata) may provide information regarding previous unknown entities suchas “Branch 3A” 252 in New York that reports to “Company 3” 250. Withthis example, one may be able to better appreciate how such data may beutilized to help fill-in, connect, and make sense out of a hierarchystructure. For example, a single piece of data may allow an entirebranch of a hierarchy to be completed, such as in the case with thepreviously unknown entity 252. The new data may allow populating andconnecting not only “Branch 3A” 252 as a child relationship with“Company 3” 250, but it may further provide defining information for allof the child relationships under “Branch 3A” 252. In other words,knowing that “Branch 3A” 252 in New York that reports to “Company 3” 250also would indicate that “Manager 3A” 254 and “Employee 3A1” 256 arealso associated with “Company 3” 250 by virtue of the existingparent-child relationships.

FIG. 2D and Table 4 also include incomplete information (indicated bydashed lines and boxes in FIG. 2D) for the additional entities: “Manager2B?” 224, “Manager 2A?” 234, “Company 2?” 220, and “Headquarters?” Forexample, at this point we may only know that “Manager 2B?” 224 exists byvirtue of the parent information provided with “Assistant 2B1” 226.Since there is not direct report information available (yet) for“Manager 2B?” 224, “Manager 2A?” 234, or “Company 2?” 220, we may make acalculated guess (based on other available information) as to theconnections 290, 292, 294 with a parent entity. However, suchconnections may be designated as “pending verification,” (as indicatedby the dashed lies).

In this illustrative example, the available information may providecompelling evidence that a “Headquarters?” 202 (or an ultimate parententity) exists in the organizational hierarchy structure. Thus, aplaceholder may be created for this entity. According to an exampleimplementation of the disclosed technology, as additional informationbecomes available (and is utilized) about the various entities of thehierarchal structure, more accurate details about the structure may beknown and recorded. Thus, we may refer back to FIG. 2A and Table 1 as acomplete, updated structure of the illustrative organization in whichthe various branches of the organization are associated with differentexample product lines.

FIG. 2E depicts an organization structure (similar to the structureshown in FIG. 2A) but having non-hierarchical connections 295, 296, 297,298, 299 for purposes of comparison. For example, the connection 295linking “Branch 1B” 206 with “Assistant 1A1” 212 bypasses the typicalhierarchical chain that may exist between “Branch 1B” 206 and “Manager1A” 210. Furthermore, “Manager 1A” 210 is shown having a parentconnection 296 with both “Branch 1B” 206 and “Branch 1A” 208, and thus,this is not a strict hierarchy. While certain non-hierarchicalconnections (for example, connections 295, 296, 297, 298, 299) may existin a structure, certain embodiments of the disclosed technology may beutilized to identify and/or flag such connections. In certain exampleembodiments, non-hierarchical connections may be ignored. In otherexample embodiments, non-hierarchical connections may be utilized tohelp determine the structure of the organization, particularly in theearly stages of process if other strict-hierarchical information is notcomplete.

FIG. 3 depicts an alternative illustrative example of a fairly complexhierarchical map where the entity data 300 is not necessarily arrangedin a visually comprehendible form. In this example illustration, theentity data 300 associated an organization identification 302 (shown inFIG. 3 as ORGID) may include fields for data such as the business unitlegal identity 304 (shown in FIG. 3 as LGID), fields for the place ofwork identifier 306 (shown in FIG. 3 as POWID), fields for the personidentification 308 (shown in FIG. 3 as a CID or ContactID), etc.

For purposes of illustration, we may assume that the business unit legalidentity 304, the place of work identifier 306, and the personidentification 308 are all on the same hierarchical level, with theorganization identification 302 in a higher level. According to anexample embodiment, the person identification 308 may be defined as allof the references to a single person (for example, an employee) at agiven organization 302. However, this definition may be viewed twocompletely different ways when the linking process is considered. Forexample: (a) only two references can be linked into a single personidentification 308 if they are already part of an organization 302 (thisis the downwards direction in terms of the hierarchy); or (b) tworeferences link together a single person identification 308 but suchlinking connections currently stem from organizations that havedifferent organization identifications 302, and thus, such informationmay be compelling enough to link the organization identifications 302(this is the upwards direction in terms of the hierarchy). Therefore,according to certain example implementations, there could be twodifferent linkage processes for each of entities on the same level: upand a down. The implication is that three different processes could becombining organization identifications 302 while three differentprocesses might be relying upon the values in them. At the same time,the organization identifications 302 may have its own entity linkingprocess that does not require input from the other three processes.

According to certain example implementations of the disclosedtechnology, an order of processing these links may be defined such that,for any given record, the links to parent (and grand parent, etc.)levels are evaluated first (the up process), then for any given record,the links to children (and grand children, etc.) may be determined, theentities may be linked or combined if compelling enough (as describedabove) and the cycle may start over to continue refining the linkingconnections. In addition, and according to certain exampleimplementations, the most mutually compelling link for any given ID-IDpair may be taken in a single iteration. Thus, in according to anexample implementation, all processes may establish their mostcompelling links prior to any of them having to make harder decisions.

In accordance with certain example implementations of the disclosedtechnology, the process of associating records or entities in ahierarchical structure may include clustering hierarchical databaserecords into a first set of clusters having corresponding first clusteridentifications (IDs), each hierarchical database record may include oneor more field values, and the clustering may be based at least in parton determining similarity among corresponding field values of thehierarchical database records.

According to an example implementation of the disclosed technology, thehierarchical database may include certain duplicate records which may(or may not) refer to the same entity. For example, the initial databasemay include a number of disjointed entities (such as depicted in FIG.2C) having connections to certain parent entities. In one exampleimplementation, the process may include determining parent-childhierarchical relationships among the hierarchical database records andassociating related hierarchical database records by applying ahierarchal directional linking process. The hierarchal directionallinking process may include selecting and applying at least an upwardprocess based on the determined parent-child hierarchical relationship.The upward process may include determining, from the parent-childhierarchical relationships, similarity among a plurality of childrecords having separate parent records.

According to an example implementation, and in response to determining athreshold similarity among that the plurality of child records, theprocess may include inferring that the separate parent recordscorrespond to the same entity. Thus, according to an exampleimplementation, similarities among certain child records may becompelling enough to infer that they have the same parent record.

In the examples presented above, in reference to FIG. 2C and FIG. 2D, asimilarity among the child records (such as the product informationshown in the right-hand column of Table 1 and Table 3) having separateparent records may be compelling enough to infer that the separateparent records (“Company X?” 222 and “Company Y? 233” correspond to thesame entity (“Company 2?” 220).

According to an example implementation, the process may includere-clustering at least a portion of the database records into a secondset of clusters having corresponding second cluster IDs. There-clustering may be based at least in part on the associating relatedhierarchical database records and on the determining similarity amongcorresponding field values of the database records. In certain exampleimplementations, the re-clustering may be based at least in part on theassociating related hierarchical database records and on the determiningsimilarity among one or more attribute identifiers associated with thedatabase records. In certain example implementations of the disclosedtechnology, the resulting database record information may be provided asoutput based at least in part on the re-clustering.

As indicated above, and according to certain example implementations ofthe disclosed technology, each hierarchical database record maycorresponds to an entity representation, and each hierarchical databaserecord may include a plurality of fields, each field may be configuredto contain a field value, and each field value may be assigned a fieldvalue weight corresponding to a specificity of the field value inrelation to all field values in a corresponding field of the records.

In certain example implementations of the disclosed technology, thehierarchal directional linking process may further include selecting andapplying a downward process (for example, in a direction from a parentto children records) that may include linking two or more records on agiven hierarchy level based at least in part on the two records sharinga common parent record.

As discussed previously, the process of determining the similarity amongthe corresponding field values of the database records may includeassigning a hyperspace attribute to each database record, wherein thehyperspace attribute corresponding to two database records is correlatedwith a similarity of the corresponding field values of the two databaserecords. The process may include determining membership of each databaserecord in a plurality of hyperspace clusters based at least in part onthe hyperspace attributes, assigning, to each record, a cluster ID and amatch value reflecting a likelihood that the record is a member of aparticular hyperspace cluster, and linking related records based atleast in part on the cluster ID and the match value. In an exampleimplementation, determining membership of each database record in theplurality of hyperspace clusters may include creating a plurality ofnodes at random locations in hyperspace, each node maintaining recordsin hyperspace based on the hyperspace attribute for which it is theclosest node.

According to an example embodiment, the process may include mergingdatabase records having hyperspace attribute differences within apredefined criteria to eliminate similar exemplars that are likely torepresent a same entity, resulting in a reduced set of database records.In certain example embodiments, the field value weights may bere-calculated for the reduced set of database records and the reducedset of records may be re-clustered based at least in part on therecalculated field value weights.

In accordance with an example implementation, associating relatedhierarchical database records may further include the process ofdetermining highest compelling linkages among the hierarchical databaserecords. For example, this process of determining highest compellinglinkages may involve identifying mutually preferred pairs of recordsfrom the hierarchical database records, where each mutually preferredpair of records consists of a first record and a second record, and thefirst record consists of a preferred record associated with the secondrecord and the second record consists of a preferred record associatedwith the first record, and the mutually preferred pairs of records eachhas a match score that meets pre-specified match criteria. The processof determining highest compelling linkages may further involveassigning, for each record from the hierarchical database records, atleast one associated preferred record, where a match value assigned to agiven record together with its associated preferred record is at leastas great as a match value assigned to the record together with any otherrecord in the database records. The process may further include formingand storing a plurality of entity representations in the database, whereeach entity representation of the plurality of entity representationsincludes at least one linked pair of mutually preferred records.

An example method 700 for external linking of records based onhierarchal level weightings will now be described with reference to theflowchart of FIG. 7. The method 700 starts in block 702, and accordingto an example implementation includes clustering hierarchical databaserecords into a first set of clusters having corresponding first clusteridentifications (IDs), each hierarchical database record comprising oneor more field values, the clustering based at least in part ondetermining similarity among corresponding field values of thehierarchical database records. In block 704, the method 700 includesdetermining parent-child hierarchical relationships among thehierarchical database records. In block 706, the method 700 includesassociating related hierarchical database records by applying ahierarchal directional linking process, the hierarchal directionallinking process including selecting and applying at least an upwardprocess based on the determined parent-child hierarchical relationship,the upward process may include determining, from the parent-childhierarchical relationships, similarity among a plurality of childrecords having separate parent records, and in response to determining athreshold similarity among the plurality of child records, inferringthat the separate parent records correspond to the same entity. In block708, the method 700 includes re-clustering at least a portion of thedatabase records into a second set of clusters having correspondingsecond cluster IDs, the re-clustering based at least in part on theassociating related hierarchical database records and on the determiningsimilarity among corresponding field values of the database records. Inblock 710, the method 700 includes outputting database recordinformation, based at least in part on the re-clustering.

In certain example implementations of the disclosed technology, thehierarchal directional linking process may further include selecting andapplying a downward process including linking two or more records on agiven hierarchy level based at least in part on the two records sharinga common parent record. In certain example implementations of thedisclosed technology, determining the similarity among the correspondingfield values of the database records may include assigning a hyperspaceattribute to each database record, wherein the hyperspace attributecorresponding to two database records is correlated with a similarity ofthe corresponding field values of the two database records. In certainexample implementations of the disclosed technology, determining thesimilarity among the corresponding field values of the database recordsmay include determining membership of each database record in aplurality of hyperspace clusters based at least in part on thehyperspace attributes. In certain example implementations of thedisclosed technology, determining the similarity among the correspondingfield values of the database records may include assigning, to eachrecord, a cluster ID and a match value reflecting a likelihood that therecord is a member of a particular hyperspace cluster and linkingrelated records based at least in part on the cluster ID and the matchvalue.

In certain example implementations of the disclosed technology, thehierarchal directional linking process may further include mergingdatabase records having hyperspace attribute differences within apredefined criteria to eliminate similar exemplars that are likely torepresent a same entity, the merging resulting in a reduced set ofdatabase records. An example embodiment may include recalculating thefield value weights for the reduced set of database records andre-clustering the reduced set of records based at least in part on therecalculated field value weights.

According to an example implementation, membership of each databaserecord in the plurality of hyperspace clusters may further includecreating a plurality of nodes at random locations in hyperspace, eachnode maintaining records in hyperspace based on the hyperspace attributefor which it is the closest node.

According to an example implementation, associating related hierarchicaldatabase records further include determining highest compelling linkagesamong the hierarchical database records. The determining may includeidentifying mutually preferred pairs of records from the hierarchicaldatabase records, each mutually preferred pair of records consisting ofa first record and a second record, the first record consisting of apreferred record associated with the second record and the second recordconsisting of a preferred record associated with the first record,wherein the mutually preferred pairs of records each has a match scorethat meets pre-specified match criteria. The determining may includeassigning, for each record from the hierarchical database records, atleast one associated preferred record, wherein a match value assigned toa given record together with its associated preferred record is at leastas great as a match value assigned to the record together with any otherrecord in the database records. The determining may further includeforming and storing a plurality of entity representations in thedatabase, each entity representation of the plurality of entityrepresentations comprising at least one linked pair of mutuallypreferred records.

According to certain example implementations, a hierarchical databaserecord may correspond to an entity representation, each hierarchicaldatabase record may include a plurality of fields, each field may beconfigured to contain a field value, and each field value may beassigned a field value weight corresponding to a specificity of thefield value in relation to all field values in a corresponding field ofthe records.

1B2. Internal Co-Convergence Using Clustering when there is Hierarchy inthe Data Structure and the Hierarchy Relationship is Known

In accordance with certain example implementations of the disclosedtechnology, a hierarchical relationships defining interrelations amongrecords in a data structure may already be known (or previouslydetermined), but additional information may be desired. In scenario, andaccording to certain example embodiments, additional information may beobtained via a co-convergence and clustering process, as previouslydescribed. For example, the process may include clustering hierarchicaldatabase records into a first set of clusters having corresponding firstcluster identifications (IDs), each hierarchical database recordincluding one or more field values, and the clustering may be based atleast in part on determining one or more similarities amongcorresponding field values of the hierarchical database records. In thisexample embodiment, clustering of the hierarchical database records maybe performed based on known (or received) parent-child hierarchicalrelationship information for the hierarchical database records. In thisexample embodiment, the process may include re-clustering at least aportion of the hierarchical database records into a second set ofclusters having corresponding second cluster IDs based at least in parton the received parent-child hierarchical relationship information.

As discussed in the previous section (for example, with respect to FIG.1), and according to an example implementation, determining thesimilarity among the corresponding field values of the hierarchicaldatabase records may include one or more of the following steps: (1)assigning a hyperspace attribute to each hierarchical database record,where the hyperspace attribute corresponding to two hierarchicaldatabase records is correlated with a similarity of the correspondingfield values of the two hierarchical database records; (2) determiningmembership of each hierarchical database record in a plurality ofhyperspace clusters based at least in part on the hyperspace attributes;(3) assigning, to each record, a cluster ID and a match value reflectinga likelihood that the record is a member of a particular hyperspacecluster; (4) linking related records based at least in part on thecluster ID and the match value; (5) merging hierarchical databaserecords having hyperspace attribute differences within a predefinedcriteria to eliminate similar exemplars that are likely to represent asame entity, the merging resulting in a reduced set of hierarchicaldatabase records; (6) recalculating the field value weights for thereduced set of hierarchical database records; and (7) re-clustering thereduced set of records based at least in part on the recalculated fieldvalue weights.

In accordance with certain example implementations of the disclosedtechnology, the process may further include determining compellinglinkages among the hierarchical database records by identifying mutuallypreferred pairs of records from the hierarchical database records, eachmutually preferred pair of records consisting of a first record and asecond record, the first record consisting of a preferred recordassociated with the second record and the second record consisting of apreferred record associated with the first record, wherein the mutuallypreferred pairs of records each has a match score that meetspre-specified match criteria. According to an example implementation,the process of determining the compelling linkages may further includeassigning, for each record from the database records, at least oneassociated preferred record, wherein a match value assigned to a givenrecord together with its associated preferred record is at least asgreat as a match value assigned to the record together with any otherrecord in the hierarchical database records. According to an exampleimplementation, the process of determining compelling linkages mayfurther include forming and storing a plurality of entityrepresentations in the database, each entity representation of theplurality of entity representations including at least one linked pairof mutually preferred records.

According to certain example embodiments of the disclosed technology,identifying mutually preferred pairs of records may involve mutuallypreferred pair of records consisting of a third record and a fourthrecord, and linking the third record to the fourth record. In an exampleimplementation, the process may include allowing a user to retrieveinformation from at least one of the third record and the fourth record.

Various embodiments described herein may further include embodimentinclude an optional process whereby each preferred record associatedwith a given record includes a record that, when paired with the givenrecord, has a maximal assigned match score in comparison to match scoresassigned to other record pairs comprising the given record. In certainexample embodiments, at least one mutually preferred pair of records mayfurther include a fifth record and a sixth record, and the process mayinclude altering at least one field value from the fifth record based onat least one field value from the sixth record. According to an exampleimplementation, the match score may reflect a number of data fieldentries common to the pair of records.

Another optional feature of the disclosed technology may include, priorto the step of linking, assigning to each pair of records from a thirdplurality of records a match score, the match score reflecting aprobability that the pair of records is related, where the secondplurality of records includes the third plurality of records,determining, for each record from a fourth plurality of records, atleast one associated preferred record, where the third plurality ofrecords includes the fourth plurality of records, where a match scoreassigned to a given record together with its associated preferred recordis at least as great as a match score assigned to the record togetherwith any other record in the third plurality of records, and identifyingmutually preferred pairs of records from the fourth plurality ofrecords, each mutually preferred pairs of records consisting of a fifthrecord and a sixth record, the fifth record consisting of a preferredrecord associated with the sixth record and the sixth record consistingof a preferred record associated with the fifth record.

Another example implementation of the disclosed technology may includeassigning a match score to a pair of records as determined by comparingdata field entries of the pair of records. For example, this exampleimplementation may include comparing only a portion of data fieldscommon to the pair of records. For example, the process may assign amatch score to a pair of records as calculated based at least on entriesin at least one data field common to each record of the pair. Exampleimplementation may involve a database that includes a fifth record and asixth record, where the fifth record is an associated preferred recordof the sixth record and where the sixth record is not an associatedpreferred record of the fifth record.

An example method 800 is now presented with reference to the flowchartof FIG. 8 for performing internal co-convergence using clustering whenthere is a hierarchy in the data structure and the hierarchyrelationship is known. The method 800 starts in block 802, and accordingto an example implementation includes clustering hierarchical databaserecords into a first set of clusters having corresponding first clusteridentifications (IDs), each hierarchical database record comprising oneor more field values, the clustering based at least in part ondetermining similarity among corresponding field values of thehierarchical database records. In block 804, the method 800 includesreceiving parent-child hierarchical relationship information for thehierarchical database records. In block 806, the method 800 includesre-clustering at least a portion of the hierarchical database recordsinto a second set of clusters having corresponding second cluster IDs,the re-clustering based at least in part on the received parent-childhierarchical relationship information. In block 808, the method 800includes outputting hierarchical database record information, based atleast in part on the re-clustering.

In accordance with an example implementation, determining the similarityamong the corresponding field values of the hierarchical databaserecords may include assigning a hyperspace attribute to eachhierarchical database record, wherein the hyperspace attributecorresponding to two hierarchical database records is correlated with asimilarity of the corresponding field values of the two hierarchicaldatabase records. In an example implementation, determining thesimilarity among the corresponding field values of the hierarchicaldatabase records may further include determining membership of eachhierarchical database record in a plurality of hyperspace clusters basedat least in part on the hyperspace attributes. In an exampleimplementation, determining the similarity among the corresponding fieldvalues of the hierarchical database records may further includeassigning, to each record, a cluster ID and a match value reflecting alikelihood that the record is a member of a particular hyperspacecluster. Example embodiments may further include linking related recordsbased at least in part on the cluster ID and the match value.

Example embodiments may further include merging hierarchical databaserecords having hyperspace attribute differences within a predefinedcriteria to eliminate similar exemplars that are likely to represent asame entity, the merging resulting in a reduced set of hierarchicaldatabase records. Example implementations may include recalculating thefield value weights for the reduced set of hierarchical database recordsand re-clustering the reduced set of records based at least in part onthe recalculated field value weights.

Certain example implementations may include determining highestcompelling linkages among the hierarchical database records. Thedetermining may include identifying mutually preferred pairs of recordsfrom the hierarchical database records, each mutually preferred pair ofrecords consisting of a first record and a second record, the firstrecord consisting of a preferred record associated with the secondrecord and the second record consisting of a preferred record associatedwith the first record, wherein the mutually preferred pairs of recordseach has a match score that meets pre-specified match criteria. Incertain example implementations, the determining may include assigning,for each record from the database records, at least one associatedpreferred record, wherein a match value assigned to a given recordtogether with its associated preferred record is at least as great as amatch value assigned to the record together with any other record in thehierarchical database records. In certain example implementations, thedetermining may include forming and storing a plurality of entityrepresentations in the database, each entity representation of theplurality of entity representations comprising at least one linked pairof mutually preferred records.

According to an example implementation, a given hierarchical databaserecord may correspond to an entity representation or an entity. In anexample implementation, each database record may include a plurality offields, each field configured to contain a field value, and each fieldvalue assigned a field value weight corresponding to a specificity ofthe field value in relation to all field values in a corresponding fieldof the records.

2. External Linking Based on Hierarchal Level Weightings

External linking, which is sometimes referred to as “entity resolution,”may be contrasted with internal linking. External linking may involve aprocess of linking information from an external file to a previouslylinked base file (or authority file) in order to assign entityidentifiers to the external data. In typical embodiments of thedisclosed technology, internal and external linking are completelydifferent processes, executed at different times for different reasons.However, an external linking process may act upon a file created by theinternal linking process—but this is not a requirement. For example, andaccording to certain example implementations, an internal linkingprocess may be utilized as initial process to characterize or group datawhen data relationships are not known beforehand. In an exampleimplementation, an external linking process may be utilized after atleast some data relationships are established by the internal linkingprocess.

FIG. 4A depicts an example implementation of an external linking process400, according to an example embodiment of the disclosed technology. Forexample, a base file 402 may be utilized in the external linking processand may include a file, table, or database with records representing oneor more entities (as indicated by the rows of the base file 402). Eachentity may be assigned a unique identifier, and the corresponding fieldsmay be populated with associated field values (while certain fields maybe left blank if the particular field value is not available or does notapply). The base file 402 may include fields such as name, address,phone, social security number, etc., along with the unique identifiersthat may signify to which entity each record belongs.

According to an example implementation of the disclosed technology, thebase file 402 may be divided (or “shredded”) into multiple tables 404.For example, in a simple implementation, the base file 402 may beutilized to populate one table for each of the fields (for example, onetable for names, one table for addresses, one table for phone numbers,etc.). In an example implementation of the disclosed technology, eachtable 404 may be scrubbed to eliminate duplicate records (deduped). Inan example implementation, each table entry may be sorted so that, foreach name, there is a sorted list of entities with that name (and foreach address there is a sorted list of entities at that address etc.)while keeping track of the unique identifiers associated with eachrecord or entity. According to an example implementation, the tables maythen be indexed to allow quick retrieval of any given record. Forexample, the index may allow rapid retrieval of an entity list for agiven address.

In an example implementation, the external linking process 400 mayfurther include receiving an input query file 406 having one or morequery terms (or query values) that may be utilized to search for andretrieve matching records from the base file 402 (or the multiple tables404). According to an example implementation of the disclosedtechnology, the input file may be divided (or “shredded”) into multiplequery tables 408. For example, in a simple implementation, the inputquery file 406 may be utilized to populate one table for each of thequery fields (for example, one table for names, one table for addresses,one table for phone numbers, etc.).

The example processes described above may be considered as preliminarysteps for preparing the base file 402 and associated tables 404 for aquery 406 using specific input criteria that may match with the fieldvalues in the tables 404.

An example external linking process may utilize a specific query file406, such as Dave Smith, 123 Main Street, New York, N.Y., 917-555-1212,052-21-1234. In an example implementation, this query input may bedivided (or “shredded”) into multiple fields 408 (similar to the way thebase file 402 is treated) and the results may be utilized to fetchmatches (or partial matches) from the previously mentioned tables 404that correspond to the base file 402 information.

In an illustrative example, suppose that the input query 406 is shreddedinto three tables 408 with data from the corresponding three fields:Name, Address, Social Security Number (for example: Dave Smith, 123 MainStreet, 052-21-1234). In this example, the query may return two entriesfor the “Social Security” (two possible entities in the base file 402having used the Social Security number of 052-21-1234), 130 peoplehaving an “Address” of 123 Main Street, and 2450 people with the “Name”of Dave Smith (since this is a common name).

According to an example implementation, the resulting lists 414 may besorted by the unique entity identifiers, merged by the identifiers, andeach entity may be evaluated for a count of the number of tables inwhich they appear. According to an example implementation, the mergedlists may then be sorted by the number of count of tables in which eachentity appeared, and the entity with the most appearances may bedeclared the best match to the information provided in the input query406.

FIG. 4B depicts an external linking process 401, in accordance with anexample implementation of the disclosed technology, in which theentities may form a strict hierarchy, and we wish to be able to performan external link for a given record in the hierarchy. According to anexample implementation, this external linking process 401 may sharecertain similarities with the process 400 described above and depictedin FIG. 4A. For example, embodiments may utilize an external linkingprocess may include rolling-up scores from the various linkpaths,similar to process described in paragraphs [0218]-[0258] in the U.S.Patent Publication 2010/0094910 that is incorporated herein byreference.

In an example implementation, the external linking process 401 mayreturn one or more matching entity records (or IDs) 420 422 424 from thehierarchical base file 416, determine which hierarchy level(s) 418 areassociated with the returned records, and attempt to resolve 428 thelowest child record 424 by performing certain tests. In one exampleimplementation, a test for resolving a record may include scoring thereturned records based on certain matching and/or uniqueness criteriaand picking the best match based on the score. In another exampleimplementation, a test for resolving a record may include determining ifthe associated score is greater than or equal to a predetermined value.In another example implementation, a test for resolving a record may bebased on a difference in scores between the first and second bestmatched records. Example embodiments may utilize various combinations ofthese tests to resolve a record.

In an example implementation, when “matching” records are returnedhaving different levels in the hierarchy 418, and when the lowest childrecord 424 is not properly resolved, the process 401 may be utilized tore-roll 426 intermediate results from the children records into theirparent records. In an example implementation, if a parent record 422resolves, then the associated record may be returned. In an exampleimplementation, if the parent record does not resolve, then results maybe “rolled-up” to the grandparent level 420, and so forth. According tocertain example embodiments, the less data that is available in the basefile 416, or the less specific the query, the more likely it is that theparent/grandparent etc. levels may be utilized to resolve a record.

Example embodiments may include associating external query data 406having one or more query field values with a record in a linkedhierarchical database. The linked hierarchical database may include aplurality of records, each record having a record identifier andrepresenting an entity in a hierarchy, each record associated with ahierarchy level, each record including one or more fields, each fieldconfigured to contain a field value. The associating may includereceiving the external query data, wherein the external query dataincludes one or more search values; and identifying, from the pluralityof records in the linked hierarchical database, one or more matchedfields having field values that at least partially match the one or moresearch values.

Example embodiment may further include scoring, with zero or more matchweights, each of the one or more matched fields; determining anaggregate weight for each matched field based at least in part on thescoring with the zero or more match weights; sorting the one or morematched fields according to the determined aggregate weights; merging,based at least in part on determining the aggregate weights, the one ormore matched fields to form a merged table having records with matchedfields sorted by aggregate weights; scoring the merged table based atleast in part on the aggregate weights; identifying, based at least inpart on the scoring, a grouping comprising one or more of the pluralityof entities within a same branch of the hierarchy and corresponding todifferent hierarchy levels; and outputting, based at least in part onthe scoring and identifying, a record identifier corresponding to amatching entity in the hierarchy.

In certain embodiments, scoring, with the zero or more match weights mayinclude scoring each of the one or more matched fields with a uniquenessweight, the uniqueness weight representing a specificity of the fieldvalue in relation to all field values in a corresponding field of theplurality of records in the linked hierarchical database. Exampleembodiments may include at least partially forming one or more searchtables corresponding to the one or more search values and at leastpartially forming one or more base tables corresponding to the one ormore fields of the plurality of records of the linked hierarchicaldatabase. In certain embodiments, the merging, based at least in part ondetermining the aggregate weights, can include combining at least aportion of the one or more search tables and the one or more base tablesto form the merged table.

According to an example implementation of the disclosed technology,partially forming the one or more base tables may include at leastpartially forming tables having multiple fields and wherein the basetables include record identifiers for each entity in the hierarchy.Certain embodiments may include sorting each entity in the hierarchy byan associated hierarchy level. In one example implementation, thesorting may include progressively sorting each entity in the hierarchyby each hierarchy level from a highest level to a lowest level in thehierarchy. In certain embodiments, the one or more search tables and/orthe one or more base tables may include zero or more common fields. Theone or more base tables may include record identifiers for each entityin the hierarchy.

Certain example embodiments may further include determining, from themerged table, and based at least in part from the aggregate weights, afirst leading scorer of the matched fields and a second leading scorerof the matched fields, the first leading scorer associated with a recordhaving a highest aggregate weight and the second leading scorerassociated with a record having a second highest aggregate weight.Example embodiments may include determining a first condition that mayinclude determining whether a first weight associated with the firstleading scorer meets or exceeds a first predetermined value. Exampleembodiments may include determining a second condition that may includedetermining whether a difference between the first weight and a secondweight associated with the second leading scorer meets or exceeds asecond predetermined value. Example embodiments may include determiningif a matching entity corresponds to the first leading scorer and may bebased at least in part on the determining of the first condition and onthe determining of the second condition.

Certain example embodiments may include merging aggregate weights of oneor more entities associated with intermediate hierarchy levels in thegrouping in response to determining that the first condition or thesecond condition is not met. Certain example embodiments may includere-determining the first condition and the second condition, andoutputting a record identifier corresponding to an entity associatedwith a lowest hierarchy level of the grouping in response tore-determining the first condition and the second condition. In certainexample embodiments, a matching entity may further correspond to anentity associated with a lowest hierarchy level associated with thegrouping. For example, information may be obtained and utilized throughimplementations of the disclosed technology that may allow resolving arecord having the lowest possible hierarchy position in grouping. Incertain embodiments, if the (child) record occupying the lowesthierarchy position in grouping is not resolved (via the tests orconditions described above), then embodiments of the disclosedtechnology may attempt to resolve the next lowest parent record, and soforth.

According to an example implementation of the disclosed technology, anaggregate weight for each field may be calculated based, at least inpart, on field values scored from each of the plurality of records inthe linked hierarchical database. As an example, suppose that a queryvalue of “H” returns a score of 2 based on matches with records in the“First Name” field from a first child record in a hierarchy. Now,suppose that for a second child record, a query value of “Harold”returns a score of 9 in the “First Name” field. The higher score mayresult because the “Harold” query includes much more specificinformation than “H”, but “H” and “Harold” may represent the same entity(i.e., such field values do not necessarily conflict with each other).Thus, the aggregate score for the field may be 9 because the informationis not necessarily additive. Now suppose that a score of 10 is returnedfor the first child record in response to a query of “84720” in the “ZipCode” field, and a score of 10 is returned for a second child record inresponse to the query of “84724.” In this situation, the aggregate scoremay be 20 because the record matches two different zip codes, and theinformation may be considered to be additive.

Certain example implementations of the disclosed technology may includeselecting and applying a downward process (for example, in a directionfrom a parent to children records) that may link two or more records ona given hierarchy level based at least in part on determining that thetwo or more records share a common parent record.

According to an example implementation of the disclosed technology, oneor more matched fields having field values that at least partially matchthe one or more search value may be identified from the plurality ofrecords in the linked hierarchical database by identifying, from the oneor more base tables, one or more matched fields having field values thatat least partially match the one or more search values. In certainexample implementations, identifying the one or more matched fields mayinclude determining highest compelling linkages among the hierarchicaldatabase records. Such a determination may include identifying mutuallypreferred pairs of records from the hierarchical database records, eachmutually preferred pair of records consisting of a first record and asecond record, the first record consisting of a preferred recordassociated with the second record and the second record consisting of apreferred record associated with the first record, wherein the mutuallypreferred pairs of records each has a match score that meetspre-specified match criteria. In certain embodiments, the one or morematched fields may be further identified by assigning, for each recordfrom the hierarchical database records, at least one associatedpreferred record, where a match value assigned to a given recordtogether with its associated preferred record is at least as great as amatch value assigned to the record together with any other record in thedatabase records. Certain embodiments may further include forming andstoring a plurality of entity representations in the database, eachentity representation of the plurality of entity representationscomprising at least one linked pair of mutually preferred records.

An example method 900 for performing external linking based onhierarchal level weightings will now be described with reference to theflowchart of FIG. 9. The method 900 starts in block 902, and accordingto an example implementation includes associating external query datahaving one or more query field values with a record in a linkedhierarchical database, the linked hierarchical database including aplurality of records, each record having a record identifier andrepresenting an entity in a hierarchy, each record associated with ahierarchy level, each record including one or more fields, each fieldconfigured to contain a field value, the associating including receivingthe external query data, wherein the external query data comprises oneor more search values; and identifying, from the plurality of records inthe linked hierarchical database, one or more matched fields havingfield values that at least partially match the one or more searchvalues. In block 904, the method 900 includes scoring, with zero or morematch weights, each of the one or more matched fields. In block 906, themethod 900 includes determining an aggregate weight for each matchedfield based at least in part on the scoring with the zero or more matchweights. In block 908, the method 900 includes sorting the one or morematched fields according to the determined aggregate weights. In block910, the method 900 includes merging, based at least in part ondetermining the aggregate weights, the one or more matched fields toform a merged table having records with matched fields sorted byaggregate weights. In block 912, the method 900 includes scoring themerged table based at least in part on the aggregate weights. In block914, the method 900 includes identifying, based at least in part on thescoring, a grouping comprising one or more of the plurality of entitieswithin a same branch of the hierarchy and corresponding to differenthierarchy levels. In block 916, the method 900 includes outputting,based at least in part on the scoring and identifying, a recordidentifier corresponding to a matching entity in the hierarchy.

According to an example implementation, scoring, with the zero or morematch weights may include scoring each of the one or more matched fieldswith a uniqueness weight, the uniqueness weight representing aspecificity of the field value in relation to all field values in acorresponding field of the plurality of records in the linkedhierarchical database.

Certain example implementations may further include at least partiallyforming one or more search tables corresponding to the one or moresearch values; and at least partially forming one or more base tablescorresponding to the one or more fields of the plurality of records ofthe linked hierarchical database. In an example implementation, merging,based at least in part on determining the aggregate weights may includecombining at least a portion of the one or more search tables and theone or more base tables to form the merged table. In an exampleimplementation, at least partially forming the one or more base tablesmay include at least partially forming tables having multiple fields andwherein the base tables comprise record identifiers for each entity inthe hierarchy. Example implementations may further include sorting eachentity in the hierarchy by an associated hierarchy level, where thesorting each entity in the hierarchy by the associated hierarchy levelincludes progressively sorting each entity in the hierarchy by eachhierarchy level from a highest level to a lowest level in the hierarchy.

According to an example implementation, the one or more search tablesmay include zero or more common fields. In certain example embodiments,the one or more base tables may include zero or more common field. Incertain example implementations, the one or more base tables may includerecord identifiers for each entity in the hierarchy.

Example embodiments of the disclosed technology may further includedetermining, from the merged table, and based at least in part from theaggregate weights, a first leading scorer of the matched fields and asecond leading scorer of the matched fields, the first leading scorerassociated with a record having a highest aggregate weight and thesecond leading scorer associated with a record having a second highestaggregate weight. Certain example implementations may includedetermining a first condition comprising whether a first weightassociated with the first leading scorer meets or exceeds a firstpredetermined value. Certain example implementations may includedetermining a second condition comprising whether a difference betweenthe first weight and a second weight associated with the second leadingscorer meets or exceeds a second predetermined value. According to anexample implementation, a matching entity may correspond to the firstleading scorer and is based at least in part on the determining of thefirst condition and on the determining of the second condition. Inaccordance with an example implementation, the matching entity mayfurther correspond to an entity associated with a lowest hierarchy levelassociated with the grouping.

Example implementations of the disclosed technology may further includemerging aggregate weights of one or more entities associated withintermediate hierarchy levels in the grouping in response to determiningthat the first condition or the second condition is not met. Certainexample embodiments may include re-determining the first condition andthe second condition, and outputting a record identifier correspondingto an entity associated with a lowest hierarchy level of the grouping inresponse to re-determining the first condition and the second condition.According to an example implementation, an aggregate weight for eachfield may be based, at least in part, on field values scored from eachof the plurality of records in the linked hierarchical database.

3. Populating Entity Fields Based on Hierarchy Partial Resolution

In accordance with certain example implementations of the disclosedtechnology, interrelations among records in a data structure may beorganized according to strict hierarchical relationships, and thehierarchy relationships and structure may already be known (orpreviously determined). In an example implementation, such relationshipsmay be utilized to enhance performance and accuracy of certainprocesses, such as those associated with form filling, searching, etc.

For example, consider a typical process for entering information in anonline form. The traditional form filling process may rely upon the userto enter correct information in form input boxes. The form may beassociated with an auto-correct and/or an autofill feature that maysuggest spelling corrections and/or automatically populate a field basedon information from a previously filled form. A typical onlineform-filling process may allow a user to enter information in the formwithout relying on the actual data in a hierarchy. As an example,consider the field-by-by field basis of a typical autofill function. Ifa user types the letters “M” and “I” in a city field of a form, the citystarting with MI and having the largest population may autofill theform. Since many cities start with the letters “MI”, unless it wasintended by the user to enter “Miami,” then the autofill may produce thewrong results, which could lead to an error in the form.

In a typical search engine, and in response to receiving a term orphrase as a query input, the search engine may rely upon previouslyindexed words that have been extracted from web page titles, pagecontent, headings, etc., and results may be returned based on relevanceto the query. For example, when a user enters a query into a searchengine, the engine may examine an index and provide a listing ofbest-matching results. However, a traditional search engine may notutilize information in a hierarchy structure to limit the scope of thesearch to results within the hierarchy.

According to an example implementation of the disclosed technology,input data may be received at a processor (for example, in response to auser typing characters into an online form field) and as the input datais received, embodiments of the disclosed technology may begin toresolve an entity in a hierarchy based on the received input data.According to an example implementation of the disclosed technology,resolving the entity, and having access to its relationship in thehierarchy may allow enhancing suggestions for further specificity asadditional query information is typed into associated form fields andreceived by the processor.

Example embodiments of the disclosed technology may provide thetechnical effect and benefit of speeding up the rate at which data maybe entered. Such a technical effect may provide enhanced customerservice, enhanced accuracy, and/or an enhanced user experience whenfilling-in forms or doing searches. Example embodiments of the disclosedtechnology may also provide form validation. For example, by offeringsuggestions, or by only allowing entry of specific data based on theinformation from the hierarchy, the form may be populated with thecorrect information so that it can be 100% correct when it is submitted.

FIG. 5A depicts example hierarchical structures 500 for illustrationpurposes. The information shown may represent data in one or more ahierarchical databases that are associated with “Widget Stores.” Forexample, the hierarchy structure depicted on the left side may includeheadquarters for “Brand 1” 502 with a child “Branch 1A” 508 havingchildren “Store 1A1” 510 and “Store 1A2” 512 at different addresses. Thehierarchy structure depicted in the center of FIG. 5A may includeheadquarters for “Brand 2” 504 with children “Branch 2B” 514 and “Branch2A” 520. “Branch 2B” 514 is depicted as having multiple children stores516 518 519.

“Branch 2A” 520 is depicted as having children “Store 2A1” 522 and“Store 2A2” 524 at different addresses. Finally, the hierarchy structuredepicted in the right side of FIG. 5A may include headquarters for“Brand 3” 506 with a child “Branch 3A” 530 having child “Store 3A1” 532.The known hierarchical relationships, as depicted in this example, maybe utilized for providing enhanced form filling and/or searchingfeatures in example embodiments, and will be explained below withreference to FIG. 5B and FIG. 5C.

FIG. 5B depicts an intermediate result of an external linking processbased on hierarchal level weightings and/or partial resolution,according to an example implementation of the disclosed technology. Forexample, a user may begin entering “Widgets New York” into a form field503. Based on the query information, and according to an exampleimplementation, the various hierarchies associated with the word“widget” may be utilized as soon as the first query term (or just a partof the term) is entered and/or received by the system and/or serverand/or processor. In an example implementation, a suggestion 505 fornarrowing the specificity of the search, or for populating a subsequentquery field may be output for display based on the first query term. Forexample, the offered suggestions 505 may include “Brand 1” “Brand 2”and/or “Brand 3” since each of these brands may be associated with theterm “widgets,” and the user may select one of the offered suggestions,rather than having to manually enter the term, thereby eliminatingerrors.

As shown in FIG. 5B, and for further illustration, after user enters“Widgets New York” into a form field 503, part of the original hierarchystructure associated with “widgets” may no longer apply, and only thosebranches 550 552 that relate to both “widgets” and “New York” need to besearched, or offered for form input suggestion.

In accordance with various example implementations of the disclosedtechnology, and in response to a particular search term (or token) beingentered into a form field 503, certain offered suggestions 505 relatedto the search term may be presented, for example, as a list, dropdownmenu, etc. In one example implementation, multiple entities may bereturned and provided in a list for selection. In certain exampleimplementations of the disclosed technology, selecting one of theoffered suggestions 505 may filter and/or sort the offered suggestions505 list for further entity specificity based on the selection.

In an example implementation, and in response to a particular searchterm (or token) being entered into a form field 503, multiple entitiesmay be provided as offered suggestions 505. For example, a user may bepresented with all child entity records 550 552 (and grandchild entityrecords, etc.) related to the search term, as depicted in FIG. 2B. Incertain example implementations of the disclosed technology, the offeredsuggestions 505 may be filtered and/or sorted based on the specificityof the particular search term (or token). In certain exampleimplementations of the disclosed technology, the specificity of thesearch term (or token) in relation to a parent entity (and/orgrandparent entity records, etc.) may also be utilized to sort and/orfilter the returned results or further offered suggestions 505.

FIG. 5C continues this illustration and depicts example results that maybe narrowed down to a specific record 554 in the hierarchy based on theinput query data and/or selected suggested terms. For example, afterentering “Widgets New York” in a form field 503, the available brands ofwidgets in New York may be offered for suggestion. The user may selectone of the brands (for example “Brand 3” in a subsequent form field 505)and in response, available types of widgets (for example “A Widgets”)may be offered for suggestion in a subsequent form field 507. Based onthis information, the user may see that the type of widget 554 she issearching for may be located at a particular location 532, such as“Store 3A1” on “50^(th) and 3^(rd).” It should be noted that theillustration provided here and described with reference to FIGS. 5A-5Cis intended to serve as a general example, and is not intended to limitthe scope of the disclosed technology.

An example method 1000 for populating entity fields based on hierarchypartial resolution will now be described with reference to the flowchartof FIG. 10. The method 1000 starts in block 1002, and according to anexample implementation includes receiving, at a computing device, afirst indication input comprising at least a portion of a first queryterm. In block 1004, the method 1000 includes identifying, based on thereceived first indication input, one or more first matching records in ahierarchical database, the hierarchical database comprising a pluralityof records, each record representing an entity in a hierarchy, eachrecord associated with a level of the hierarchy, each record comprisingone or more fields, each field of the one or more fields configured tocontain a field value, the one or more first matching records comprisingone or more fields having an associated first matching field value thatat least partially matches the received portion of the first query term.In block 1006, the method 1000 includes outputting, for display, one ormore first matching field values of the one or more first matchingrecords. In block 1008, the method 1000 includes receiving, at thecomputing device, a second indication input signifying a selection ofone of the one or more first matching field values. In block 1010, themethod 1000 includes receiving, at the computing device, a thirdindication input comprising at least a portion of a second query term.In block 1012, the method 1000 includes identifying, based on thereceived second indication input and the third indication input, zero ormore second matching records in the hierarchical database, the zero ormore second matching records comprising one or more fields having anassociated second matching field value that at least partially matchesthe received portion of the second query term, wherein the zero or moresecond matching records comprise child hierarchy records associated withthe one or more first matching records. In block 1014, the method 1000includes outputting, for display, zero or more second matching fieldvalues of the zero or more second matching records.

In certain example implementations of the disclosed technology, the zeroor more second matching records, as referred-to above, may include onlychild hierarchy records associated with the one or more first matchingrecords.

Certain example implementations may further include determining anuppermost hierarchical level corresponding to the one or more firstmatching records. In certain example implementations of the disclosedtechnology, outputting, for display, the one or more first matchingfield values associated with the one or more first matching records mayfurther include outputting one or more first matching field values thatare associated the uppermost hierarchical level.

According to an example implementation, identifying the one or morefirst matching records or the zero or more second matching records mayinclude any number of processes, including, but not limited to fuzzymatching, string metrics (such as Levenshtein distance), phoneticprocesses or algorithms (such as Metaphone), etc. In certain exampleimplementations, identifying the matching records may include a cascadeprocess that utilizes multiple processes.

According to an example implementation, the disclosed technology mayinclude outputting, for display, the one or more first matching fieldvalues of the one or more first matching records comprises a progressivecorrection of the first indication input. In certain exampleimplementations of the disclosed technology, outputting, for display,the zero or more second matching field values of the zero or more secondmatching records includes a progressive correction of the thirdindication input.

Certain example implementations may include receiving, at a computingdevice, a fourth indication input signifying a selection of a secondmatching field value and identifying, based on the received fourthindication input and the third indication input, zero or more thirdmatching records in the hierarchical database, the zero or more thirdmatching records may include one or more fields having an associatedthird matching field value that at least partially matches the receivedportion of the second query term, wherein the zero or more thirdmatching records may include child hierarchy records associated with thezero or more second matching records. Certain example implementationsmay further include outputting, for display, zero or more third matchingfield values of the zero or more third matching records.

The various embodiments disclosed herein may provide the technicaleffect of increasing speed and/or accuracy of variouscomputer-implemented applications, including but not limited to dataanalytics, entity resolution, entity searching, and/or removal ofduplicate records.

Preferably, the embodiments described herein provide forcomputer-implemented systems and/or methods to be performed using one ormore computer processors. In certain example implementations,specialized computer systems may be preferable, for example, to handlethe processing of databases with large amounts of data and/or to providerelatively fast processing speeds, etc.

In some instances, the systems described herein may include one or morecomputing devices that may be utilized to perform the methods and/orprocesses described herein. Example computing devices, as disclosedherein, may be referred to as one or more of a: desktop computer,server, laptop computer, tablet computer, set-top box, television,appliance, game device, medical device, display device, or some otherlike terminology including a mobile device, mobile computing device, amobile station (MS), terminal, cellular phone, cellular handset,personal digital assistant (PDA), smartphone, wireless phone, organizer,handheld computer, In other instances, a computing device may be aprocessor, controller, or a central processing unit (CPU). In yet otherinstances, a computing device may be a set of hardware components.

The various aspects described herein are presented as methods, devices(or apparatus), systems, and articles of manufacture that may include anumber of components, elements, members, modules, nodes, peripherals, orthe like. Further, these methods, devices, systems, and articles ofmanufacture may include or not include additional components, elements,members, modules, nodes, peripherals, or the like.

In some instances, a graphical user interface may be utilized herein andreferred to as an object-oriented user interface, an applicationoriented user interface, a web-based user interface, a touch-based userinterface, or a virtual keyboard. Certain example embodiments mayinclude a presence-sensitive display, as discussed herein, which may bea display that accepts input by the proximity of a finger, a stylus, oran object near the display. For example, a user may provide an input toa computing device by touching the surface of a presence-sensitivedisplay using a finger. In another example implementation, a user mayprovide input to a computing device by gesturing without physicallytouching any object. For example, a gesture may be received via a videocamera or depth camera.

In some instances, a presence-sensitive display can have two mainattributes. First, it may enable a user to interact directly with whatis displayed, rather than indirectly via a pointer controlled by a mouseor touchpad. Secondly, it may allow a user to interact without requiringany intermediate device that would need to be held in the hand. Suchdisplays may be attached to computers, or to networks as terminals. Suchdisplays may also play a prominent role in the design of digitalappliances such as the personal digital assistant (PDA), satellitenavigation devices, mobile phones, and video games. Further, suchdisplays may include a capture device and a display.

According to one example implementation, the terms computing device ormobile computing device, as used herein, may be a CPU, or conceptualizedas a CPU (for example, the CPU 1102 of FIG. 11). In certain exampleimplementations, the computing device (CPU) may be coupled, connected,and/or in communication with one or more peripheral devices, such asdisplay, navigation system, stereo, entertainment center, Wi-Fi accesspoint, etc. In another example implementation, the term computing deviceor mobile computing device, as used herein, may refer to a mobilecomputing device, such as a smartphone, mobile station (MS), terminal,cellular phone, cellular handset, personal digital assistant (PDA),smartphone, wireless phone, organizer, handheld computer, desktopcomputer, laptop computer, tablet computer, set-top box, television,appliance, game device, medical device, display device, or some otherlike terminology. In an example embodiment, the mobile computing devicemay output content to its local display and/or speaker(s). In anotherexample implementation, the mobile computing device may output contentto an external display device (e.g., over Wi-Fi) such as a TV or anexternal computing system.

Furthermore, the various aspects described herein may be implementedusing standard programming or engineering techniques to producesoftware, firmware, hardware, or any combination thereof to control acomputing device to implement the disclosed subject matter. The term“article of manufacture” as used herein is intended to encompass acomputer program accessible from any computing device, carrier, ormedia. For example, a computer-readable medium may include: a magneticstorage device such as a hard disk, a floppy disk or a magnetic strip;an optical disk such as a compact disk (CD) or digital versatile disk(DVD); a smart card; and a flash memory device such as a card, stick orkey drive. Additionally, it should be appreciated that a carrier wavemay be employed to carry computer-readable electronic data includingthose used in transmitting and receiving electronic data such aselectronic mail (e-mail) or in accessing a computer network such as theInternet or a local area network (LAN). Of course, a person of ordinaryskill in the art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Various implementations of the communication systems and methods hereinmay be embodied in non-transitory computer readable media for executionby a processor. An example implementation may be used in an applicationof a mobile computing device, such as a smartphone or tablet, but othercomputing devices may also be used, such as to portable computers,tablet PCs, Internet tablets, PDAs, ultra mobile PCs (UMPCs), etc.

FIG. 11 depicts a block diagram of an illustrative computing device 1100according to an example implementation. Various implementations andmethods herein may be embodied in non-transitory computer readable mediafor execution by a processor. It will be understood that the computingdevice 1100 is provided for example purposes only and does not limit thescope of the various implementations of the communication systems andmethods.

The computing device 1100 of FIG. 11 includes a central processing unit(CPU) 1102, where computer instructions are processed; a displayinterface 1104 that acts as a communication interface and providesfunctions for rendering video, graphics, images, and texts on thedisplay. In certain example implementations of the disclosed technology,the display interface 1108 may be directly connected to a local display1107. In another example implementation, the display interface 1108 maybe configured for providing data, images, and other information for anexternal/remote display 1150 that is not necessarily physicallyconnected to the mobile computing device. For example, a desktop monitormay be utilized for mirroring graphics and other information that ispresented on a mobile computing device. In certain exampleimplementations, the display interface 1108 may wirelessly communicate,for example, via a Wi-Fi channel or other available network connectioninterface 1112 to the external/remote display 1150.

In an example implementation, the network connection interface 1112 maybe configured as a communication interface and may provide functions forrendering video, graphics, images, text, other information, or anycombination thereof on the display. In one example, a communicationinterface may include a serial port, a parallel port, a general purposeinput and output (GPIO) port, a game port, a universal serial bus (USB),a micro-USB port, a high definition multimedia (HDMI) port, a videoport, an audio port, a Bluetooth port, a near-field communication (NFC)port, another like communication interface, or any combination thereof.

The computing device 1100 may include a keyboard interface 1106 thatprovides a communication interface to a keyboard. In one exampleimplementation, the computing device 1100 may include apresence-sensitive display interface 1108 for connecting to apresence-sensitive display 1107. According to certain exampleimplementations of the disclosed technology, the presence-sensitivedisplay interface 1108 may provide a communication interface to variousdevices such as a pointing device, a touch screen, a depth camera, etc.which may or may not be associated with a display.

The computing device 1100 may be configured to use an input device viaone or more of input/output interfaces (for example, the keyboardinterface 1106, the display interface 1108, the presence sensitivedisplay interface 1108, network connection interface 1112, camerainterface 1114, sound interface 1116, etc.,) to allow a user to captureinformation into the computing device 1100. The input device may includea mouse, a trackball, a directional pad, a track pad, a touch-verifiedtrack pad, a presence-sensitive track pad, a presence-sensitive display,a scroll wheel, a digital camera, a digital video camera, a web camera,a microphone, a sensor, a smartcard, and the like. Additionally, theinput device may be integrated with the computing device 1100 or may bea separate device. For example, the input device may be anaccelerometer, a magnetometer, a digital camera, a microphone, and anoptical sensor.

Example implementations of the computing device 1100 may include anantenna interface 1110 that provides a communication interface to anantenna; a network connection interface 1112 that provides acommunication interface to a network. As mentioned above, the displayinterface 1108 may be in communication with the network connectioninterface 1112, for example, to provide information for display on aremote display that is not directly connected or attached to the system.In certain implementations, a camera interface 1114 is provided thatacts as a communication interface and provides functions for capturingdigital images from a camera. In certain implementations, a soundinterface 1116 is provided as a communication interface for convertingsound into electrical signals using a microphone and for convertingelectrical signals into sound using a speaker. According to exampleimplementations, a random access memory (RAM) 1118 is provided, wherecomputer instructions and data may be stored in a volatile memory devicefor processing by the CPU 1102.

According to an example implementation, the computing device 1100includes a read-only memory (ROM) 1120 where invariant low-level systemcode or data for basic system functions such as basic input and output(I/O), startup, or reception of keystrokes from a keyboard are stored ina non-volatile memory device. According to an example implementation,the computing device 1100 includes a storage medium 1122 or othersuitable type of memory (e.g. such as RAM, ROM, programmable read-onlymemory (PROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), magneticdisks, optical disks, floppy disks, hard disks, removable cartridges,flash drives), where the files include an operating system 1124,application programs 1126 (including, for example, a web browserapplication, a widget or gadget engine, and or other applications, asnecessary) and data files 1128 are stored. According to an exampleimplementation, the computing device 1100 includes a power source 1130that provides an appropriate alternating current (AC) or direct current(DC) to power components. According to an example implementation, thecomputing device 1100 includes and a telephony subsystem 1132 thatallows the device 1100 to transmit and receive sound over a telephonenetwork. The constituent devices and the CPU 1102 communicate with eachother over a bus 1134.

In accordance with an example implementation, the CPU 1102 hasappropriate structure to be a computer processor. In one arrangement,the computer CPU 1102 may include more than one processing unit. The RAM1118 interfaces with the computer bus 1134 to provide quick RAM storageto the CPU 1102 during the execution of software programs such as theoperating system application programs, and device drivers. Morespecifically, the CPU 1102 loads computer-executable process steps fromthe storage medium 1122 or other media into a field of the RAM 1118 inorder to execute software programs. Data may be stored in the RAM 1118,where the data may be accessed by the computer CPU 1102 duringexecution. In one example configuration, the device 1100 includes atleast 128 MB of RAM, and 256 MB of flash memory.

The storage medium 1122 itself may include a number of physical driveunits, such as a redundant array of independent disks (RAID), a floppydisk drive, a flash memory, a USB flash drive, an external hard diskdrive, thumb drive, pen drive, key drive, a High-Density DigitalVersatile Disc (HD-DVD) optical disc drive, an internal hard disk drive,a Blu-Ray optical disc drive, or a Holographic Digital Data Storage(HDDS) optical disc drive, an external mini-dual in-line memory module(DIMM) synchronous dynamic random access memory (SDRAM), or an externalmicro-DIMM SDRAM. Such computer readable storage media allow the device1100 to access computer-executable process steps, application programsand the like, stored on removable and non-removable memory media, tooff-load data from the device 1100 or to upload data onto the device1100. A computer program product, such as one utilizing a communicationsystem may be tangibly embodied in storage medium 1122, which maycomprise a machine-readable storage medium.

According to one example implementation, the term computing device, asused herein, may be a CPU, or conceptualized as a CPU (for example, theCPU 1102 of FIG. 11). In this example implementation, the computingdevice (CPU) may be coupled, connected, and/or in communication with oneor more peripheral devices, such as display. In another exampleimplementation, the term computing device, as used herein, may refer toa mobile computing device, such as a smartphone or tablet computer. Inthis example embodiment, the computing device may output content to itslocal display and/or speaker(s). In another example implementation, thecomputing device may output content to an external display device (e.g.,over Wi-Fi) such as a TV or an external computing system.

In example implementations of the disclosed technology, the computingdevice 1100 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Inexample implementations, one or more I/O interfaces may facilitatecommunication between the computing device 1100 and one or moreinput/output devices. For example, a universal serial bus port, a serialport, a disk drive, a CD-ROM drive, and/or one or more user interfacedevices, such as a display, keyboard, keypad, mouse, control panel,touch screen display, microphone, etc., may facilitate user interactionwith the computing device 1100. The one or more I/O interfaces may beutilized to receive or collect data and/or user instructions from a widevariety of input devices. Received data may be processed by one or morecomputer processors as desired in various implementations of thedisclosed technology and/or stored in one or more memory devices.

One or more network interfaces may facilitate connection of thecomputing device 1100 inputs and outputs to one or more suitablenetworks and/or connections; for example, the connections thatfacilitate communication with any number of sensors associated with thesystem. The one or more network interfaces may further facilitateconnection to one or more suitable networks; for example, a local areanetwork, a wide area network, the Internet, a cellular network, a radiofrequency network, a Bluetooth enabled network, a Wi-Fi enabled network,a satellite-based network any wired network, any wireless network, etc.,for communication with external devices and/or systems.

As desired, implementations of the disclosed technology may include thecomputing device 1100 with more or less of the components illustrated inFIG. 11.

Certain implementations of the disclosed technology are described abovewith reference to block and flow diagrams of systems and methods and/orcomputer program products according to example implementations of thedisclosed technology. It will be understood that one or more blocks ofthe block diagrams and flow diagrams, and combinations of blocks in theblock diagrams and flow diagrams, respectively, can be implemented bycomputer-executable program instructions. Likewise, some blocks of theblock diagrams and flow diagrams may not necessarily need to beperformed in the order presented, or may not necessarily need to beperformed at all, according to some implementations of the disclosedtechnology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks. As an example, implementations of the disclosed technologymay provide for a computer program product, comprising a computer-usablemedium having a computer-readable program code or program instructionsembodied therein, said computer-readable program code adapted to beexecuted to implement one or more functions specified in the flowdiagram block or blocks. The computer program instructions may also beloaded onto a computer or other programmable data processing apparatusto cause a series of operational elements or steps to be performed onthe computer or other programmable apparatus to produce acomputer-implemented process such that the instructions that execute onthe computer or other programmable apparatus provide elements or stepsfor implementing the functions specified in the flow diagram block orblocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, can be implemented by special-purpose, hardware-based computersystems that perform the specified functions, elements or steps, orcombinations of special-purpose hardware and computer instructions.

Certain implementations of the disclosed technology are described abovewith reference to mobile computing devices. Those skilled in the artrecognize that there are several categories of mobile devices, generallyknown as portable computing devices that can run on batteries but arenot usually classified as laptops. For example, mobile devices caninclude, but are not limited to portable computers, tablet PCs, Internettablets, PDAs, ultra mobile PCs (UMPCs) and smartphones.

While certain implementations of the disclosed technology have beendescribed in connection with what is presently considered to be the mostpractical and various implementations, it is to be understood that thedisclosed technology is not to be limited to the disclosedimplementations, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the scope ofthe appended claims. Although specific terms are employed herein, theyare used in a generic and descriptive sense only and not for purposes oflimitation.

This written description uses examples to disclose certainimplementations of the disclosed technology, including the best mode,and also to enable any person skilled in the art to practice certainimplementations of the disclosed technology, including making and usingany devices or systems and performing any incorporated methods. Thepatentable scope of certain implementations of the disclosed technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

1. A computer-implemented method comprising: associating external query data having one or more query field values with a record in a linked hierarchical database, the linked hierarchical database comprising a plurality of records, each record having a record identifier and representing an entity in a hierarchy, each record associated with a hierarchy level, each record comprising one or more fields, each field configured to contain a field value, the associating comprising: receiving the external query data, wherein the external query data comprises one or more search values; and identifying, from the plurality of records in the linked hierarchical database, one or more matched fields having field values that at least partially match the one or more search values; scoring, with zero or more match weights, each of the one or more matched fields; determining an aggregate weight for each matched field based at least in part on the scoring with the zero or more match weights; sorting the one or more matched fields according to the determined aggregate weights; merging, based at least in part on determining the aggregate weights, the one or more matched fields to form a merged table having records with matched fields sorted by aggregate weights; scoring the merged table based at least in part on the aggregate weights; identifying, based at least in part on the scoring, a grouping comprising one or more of the plurality of entities within a same branch of the hierarchy and corresponding to different hierarchy levels; and outputting, based at least in part on the scoring and identifying, a record identifier corresponding to a matching entity in the hierarchy.
 2. The method of claim 1, wherein scoring, with the zero or more match weights, comprises scoring each of the one or more matched fields with a uniqueness weight, the uniqueness weight representing a specificity of the field value in relation to all field values in a corresponding field of the plurality of records in the linked hierarchical database.
 3. The method of claim 1, further comprising: at least partially forming one or more search tables corresponding to the one or more search values; and at least partially forming one or more base tables corresponding to the one or more fields of the plurality of records of the linked hierarchical database; and wherein the merging, based at least in part on determining the aggregate weights, comprises combining at least a portion of the one or more search tables and the one or more base tables to form the merged table.
 4. The method of claim 3, wherein at least partially forming the one or more base tables comprises at least partially forming tables having multiple fields and wherein the base tables comprise record identifiers for each entity in the hierarchy and wherein the method further comprises sorting each entity in the hierarchy by an associated hierarchy level, wherein the sorting each entity in the hierarchy by the associated hierarchy level comprises progressively sorting each entity in the hierarchy by each hierarchy level from a highest level to a lowest level in the hierarchy.
 5. The method of claim 3, wherein the one or more search tables comprise zero or more common fields, and wherein the one or more base tables comprise zero or more common fields, and wherein the one or more base tables comprise record identifiers for each entity in the hierarchy.
 6. The method of claim 1, further comprising: determining, from the merged table, and based at least in part from the aggregate weights, a first leading scorer of the matched fields and a second leading scorer of the matched fields, the first leading scorer associated with a record having a highest aggregate weight and the second leading scorer associated with a record having a second highest aggregate weight; determining a first condition comprising whether a first weight associated with the first leading scorer meets or exceeds a first predetermined value; determining a second condition comprising whether a difference between the first weight and a second weight associated with the second leading scorer meets or exceeds a second predetermined value; and wherein the matching entity corresponds to the first leading scorer and is based at least in part on the determining of the first condition and on the determining of the second condition.
 7. The method of claim 6, wherein the matching entity further corresponds to an entity associated with a lowest hierarchy level associated with the grouping.
 8. The method of claim 6, further comprising; merging aggregate weights of one or more entities associated with intermediate hierarchy levels in the grouping in response to determining that the first condition or the second condition is not met; re-determining the first condition and the second condition; and outputting a record identifier corresponding to an entity associated with a lowest hierarchy level of the grouping in response to re-determining the first condition and the second condition.
 9. The method of claim 1, wherein the aggregate weight for each field is based, at least in part, on field values scored from each of the plurality of records in the linked hierarchical database.
 10. A system comprising: a memory for storing data and computer-executable instructions; and at least one processor configured to access the memory, wherein the at least one processor is further configured to execute the computer-executable instructions to cause the system to perform a method comprising: associating external query data having one or more query field values with a record in a linked hierarchical database, the linked hierarchical database comprising a plurality of records, each record having a record identifier and representing an entity in a hierarchy, each record associated with a hierarchy level, each record comprising one or more fields, each field configured to contain a field value, the associating comprising: receiving the external query data, wherein the external query data comprises one or more search values; and identifying, from the plurality of records in the linked hierarchical database, one or more matched fields having field values that at least partially match the one or more search values; scoring, with zero or more match weights, each of the one or more matched fields; determining an aggregate weight for each matched field based at least in part on the scoring with the zero or more match weights; sorting the one or more matched fields according to the determined aggregate weights; merging, based at least in part on determining the aggregate weights, the one or more matched fields to form a merged table having records with matched fields sorted by aggregate weights; scoring the merged table based at least in part on the aggregate weights; identifying, based at least in part on the scoring, a grouping comprising one or more of the plurality of entities within a same branch of the hierarchy and corresponding to different hierarchy levels; and outputting, based at least in part on the scoring and identifying, a record identifier corresponding to a matching entity in the hierarchy.
 11. The system of claim 10, wherein scoring, with the zero or more match weights, comprises scoring each of the one or more matched fields with a uniqueness weight, the uniqueness weight representing a specificity of the field value in relation to all field values in a corresponding field of the plurality of records in the linked hierarchical database.
 12. The system of claim 10, wherein the at least one processor is further configured to execute the computer-executable instructions to cause the system to perform the method further comprising: at least partially forming one or more search tables corresponding to the one or more search values; and at least partially forming one or more base tables corresponding to the one or more fields of the plurality of records of the linked hierarchical database; and wherein the merging, based at least in part on determining the aggregate weights, comprises combining at least a portion of the one or more search tables and the one or more base tables to form the merged table.
 13. The system of claim 12, wherein at least partially forming the one or more base tables comprises at least partially forming tables having multiple fields and wherein the base tables comprise record identifiers for each entity in the hierarchy and wherein the method further comprises sorting each entity in the hierarchy by an associated hierarchy level, wherein the sorting each entity in the hierarchy by the associated hierarchy level comprises progressively sorting each entity in the hierarchy by each hierarchy level from a highest level to a lowest level in the hierarchy.
 14. The system of claim 12, wherein the one or more search tables comprise zero or more common fields, and wherein the one or more base tables comprise zero or more common fields, and wherein the one or more base tables comprise record identifiers for each entity in the hierarchy.
 15. The system of claim 10, wherein the at least one processor is further configured to execute the computer-executable instructions to cause the system to perform the method further comprising: determining, from the merged table, and based at least in part from the aggregate weights, a first leading scorer of the matched fields and a second leading scorer of the matched fields, the first leading scorer associated with a record having a highest aggregate weight and the second leading scorer associated with a record having a second highest aggregate weight; determining a first condition comprising whether a first weight associated with the first leading scorer meets or exceeds a first predetermined value; determining a second condition comprising whether a difference between the first weight and a second weight associated with the second leading scorer meets or exceeds a second predetermined value; and wherein the matching entity corresponds to the first leading scorer and is based at least in part on the determining of the first condition and on the determining of the second condition.
 16. The system of claim 15, wherein the matching entity further corresponds to an entity associated with a lowest hierarchy level associated with the grouping.
 17. The system of claim 15, wherein the at least one processor is further configured to execute the computer-executable instructions to cause the system to perform the method further comprising: merging aggregate weights of one or more entities associated with intermediate hierarchy levels in the grouping in response to determining that the first condition or the second condition is not met; re-determining the first condition and the second condition; and outputting a record identifier corresponding to an entity associated with a lowest hierarchy level of the grouping in response to re-determining the first condition and the second condition.
 18. The system of claim 10, wherein the aggregate weight for each field is based, at least in part, on field values scored from each of the plurality of records in the linked hierarchical database.
 19. A non-transient computer-readable medium that stores instructions, that when executed by a computer device having one or more processors, cause the computer device to perform a method comprising: associating external query data having one or more query field values with a record in a linked hierarchical database, the linked hierarchical database comprising a plurality of records, each record having a record identifier and representing an entity in a hierarchy, each record associated with a hierarchy level, each record comprising one or more fields, each field configured to contain a field value, the associating comprising: receiving the external query data, wherein the external query data comprises one or more search values; and identifying, from the plurality of records in the linked hierarchical database, one or more matched fields having field values that at least partially match the one or more search values; scoring, with zero or more match weights, each of the one or more matched fields; determining an aggregate weight for each matched field based at least in part on the scoring with the zero or more match weights; sorting the one or more matched fields according to the determined aggregate weights; merging, based at least in part on determining the aggregate weights, the one or more matched fields to form a merged table having records with matched fields sorted by aggregate weights; scoring the merged table based at least in part on the aggregate weights; identifying, based at least in part on the scoring, a grouping comprising one or more of the plurality of entities within a same branch of the hierarchy and corresponding to different hierarchy levels; and outputting, based at least in part on the scoring and identifying, a record identifier corresponding to a matching entity in the hierarchy.
 20. The non-transient computer-readable medium of claim 19, wherein the method further comprises: at least partially forming one or more search tables corresponding to the one or more search values; and at least partially forming one or more base tables corresponding to the one or more fields of the plurality of records of the linked hierarchical database; and wherein the merging, based at least in part on determining the aggregate weights, comprises combining at least a portion of the one or more search tables and the one or more base tables to form the merged table, and wherein at least partially forming the one or more base tables comprises at least partially forming tables having multiple fields and wherein the base tables comprise record identifiers for each entity in the hierarchy and wherein the method further comprises: sorting each entity in the hierarchy by an associated hierarchy level, wherein the sorting each entity in the hierarchy by the associated hierarchy level comprises progressively sorting each entity in the hierarchy by each hierarchy level from a highest level to a lowest level in the hierarchy.
 21. The non-transient computer-readable medium of claim 19, wherein the method further comprises: determining, from the merged table, and based at least in part from the aggregate weights, a first leading scorer of the matched fields and a second leading scorer of the matched fields, the first leading scorer associated with a record having a highest aggregate weight and the second leading scorer associated with a record having a second highest aggregate weight; determining a first condition comprising whether a first weight associated with the first leading scorer meets or exceeds a first predetermined value; determining a second condition comprising whether a difference between the first weight and a second weight associated with the second leading scorer meets or exceeds a second predetermined value; and wherein the matching entity corresponds to the first leading scorer and is based at least in part on the determining of the first condition and on the determining of the second condition, and wherein the matching entity further corresponds to an entity associated with a lowest hierarchy level associated with the grouping.
 22. The non-transient computer-readable medium of claim 19, wherein the method further comprises: merging aggregate weights of one or more entities associated with intermediate hierarchy levels in the grouping in response to determining that the first condition or the second condition is not met; re-determining the first condition and the second condition; and outputting a record identifier corresponding to an entity associated with a lowest hierarchy level of the grouping in response to re-determining the first condition and the second condition. 